Aberni Y, Boubchir L and Daachi B. 2020. Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization. Pattern Recognition Letters, 136: 101-110[DOI:10.1016/j.patrec.2020.05.030]

Abramovich G, Harding K G and Hu Q Y. 2013. Method and system for contactless fingerprint detection and verification[EB/OL]. [2021-02-03] . https://patents.google.com/patent/US8406487B2/en https://patents.google.com/patent/US8406487B2/en

Afchar D, Nozick V, Yamagishi J and Echizen I. 2018. MesoNet: a compact facial video forgery detection network//Proceedings of 2018 IEEE International Workshop on Information Forensics and Security. Hong Kong, China: IEEE: 1-7[ DOI: 10.1109/WIFS.2018.8630761 http://dx.doi.org/10.1109/WIFS.2018.8630761 ]

Agarwal A, Singh R, Vatsa M and Ratha N. 2018. Are image-agnostic universal adversarial perturbations for face recognition difficult to detect//Proceedings of the 9th IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS). Redondo Beach, USA: IEEE: 1-7[ DOI: 10.1109/BTAS.2018.8698548 http://dx.doi.org/10.1109/BTAS.2018.8698548 ]

Ahmad F, Cheng L M and Khan A. 2020. Lightweight and privacy-preserving template generation for palm-vein-based human recognition. IEEE Transactions on Information Forensics and Security, 15: 184-194[DOI:10.1109/TIFS.2019.2917156]

Ahmadi M and Soleimani H. 2019. Palmprint image registration using convolutional neural networks and Hough transform[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1904.00579.pdf https://arxiv.org/pdf/1904.00579.pdf

Ahonen T, Hadid A and Pietikainen M. 2006. Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 2037-2041[DOI:10.1109/TPAMI.2006.244]

Akhtar N and Mian A. 2018. Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access, 6: 14410-14430[DOI:10.1109/ACCESS.2018.2807385]

Anguera X, Bozonnet S, Evans N, Fredouille C, Friedland G and Vinyals O. 2012. Speaker diarization: a review of recent research. IEEE Transactions on Audio, Speech, and Language Processing, 20(2): 356-370[DOI:10.1109/TASL.2011.2125954]

Anjos A, Chakka M M and Marcel S. 2014. Motion-based counter-measures to photo attacks in face recognition. IET Biometrics, 3(3): 147-158[DOI:10.1049/iet-bmt.2012.0071]

Arjovsky M, Chintala S and Bottou L. 2017. Wasserstein generative adversarial networks//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: PMLR: 214-223

Arsalan M, Kim D S, Lee M B, Owais M and Park K R. 2019. FRED-Net: fully residual encoder-decoder network for accurate iris segmentation. Expert Systems with Applications, 122: 217-241[DOI:10.1016/j.eswa.2019.01.010]

Atoum Y, Liu Y J, Jourabloo A and Liu X M. 2017. Face anti-spoofing using patch and depth-based CNNs//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 319-328[ DOI: 10.1109/BTAS.2017.8272713 http://dx.doi.org/10.1109/BTAS.2017.8272713 ]

Auckenthaler R, Carey M and Lloyd-Thomas H. 2000. Score normalization for text-independent speaker verification systems. Digital Signal Processing, 10(1/3): 42-54[DOI:10.1006/dspr.1999.0360]

Auksorius E and Boccara A C. 2015. Fingerprint imaging from the inside of a finger with full-field optical coherence tomography. Biomedical Optics Express, 6(11): 4465-4471[DOI:10.1364/boe.6.004465]

Badrinath G S and Gupta P. 2012. Palmprint based recognition system using phase-difference information. Future Generation Computer Systems, 28(1): 287-305[DOI:10.1016/j.future.2010.11.029]

Bai S, Bai X and Tian Q. 2017. Scalable person re-identification on supervised smoothed manifold//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 3356-3365[ DOI: 10.1109/CVPR.2017.358 http://dx.doi.org/10.1109/CVPR.2017.358 ]

Bai S. 2017. Growing random forest on deep convolutional neural networks for scene categorization. Expert Systems with Applications, 71: 279-287[DOI:10.1016/j.eswa.2016.10.038]

Bak S and Carr P. 2017. One-shot metric learning for person re-identification//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 1571-1580[ DOI: 10.1109/CVPR.2017.171 http://dx.doi.org/10.1109/CVPR.2017.171 ]

Bashir K, Xiang T and Gong S G. 2010. Gait recognition without subject cooperation. Pattern Recognition Letters, 31(13): 2052-2060[DOI:10.1016/j.patrec.2010.05.027]

Basri R and Jacobs D W. 2003. Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2): 218-233[DOI:10.1109/TPAMI.2003.1177153]

Belhumeur P N, Hespanha J P and Kriegman D J. 1997. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 711-720[DOI:10.1109/34.598228]

BenAbdelkader C, Cutler R, Nanda H and Davis L. 2001. Eigengait: motion-based recognition of people using image self-similarity//Proceedings of International Conference on Audio-and Video-based Biometric Person Authentication. Halmstad, Sweden: Springer: 284-294[ DOI: 10.1007/3-540-45344-X_42 http://dx.doi.org/10.1007/3-540-45344-X_42 ]

Bergmüller T, Christopoulos E, Fehrenbach K, Schnöll M and Uhl A. 2017. Recompression effects in iris recognition. Image and Vision Computing, 58: 142-157[DOI:10.1016/j.imavis.2016.08.003]

Besl P J and McKay N D. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2): 239-256[DOI:10.1109/34.121791]

Bharathi S and Sudhakar R. 2019. Biometric recognition using finger and palm vein images. Soft Computing, 23(6): 1843-1855[DOI:10.1007/s00500-018-3295-6]

Bie F H, Wang D, Zheng T F, Tejedor J and Chen R X. 2013. Emotional adaptive training for speaker verification//Proceedings of 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. Kaohsiung, China: IEEE: 1-4[ DOI: 10.1109/APSIPA.2013.6694123 http://dx.doi.org/10.1109/APSIPA.2013.6694123 ]

Blanz V and Vetter T. 2003. Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9): 1063-1074[DOI:10.1109/TPAMI.2003.1227983]

Bloom B H. 1970. Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7): 422-426[DOI:10.1145/362686.362692]

Bobick A F and Davis J W. 2001. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3): 257-267[DOI:10.1109/34.910878]

Bodor R, Drenner A, Fehr D, Masoud O and Papanikolopoulos N. 2009. View-independent human motion classification using image-based reconstruction. Image and Vision Computing, 27(8): 1194-1206[DOI:10.1109/34.910878]

Bose A J and Aarabi P. 2018. Adversarial attacks on face detectors using neural net based constrained optimization//Proceedings of the 20th IEEE International Workshop on Multimedia Signal Processing (MMSP). Vancouver, Canada: IEEE: 1-6[ DOI: 10.1109/MMSP.2018.8547128 http://dx.doi.org/10.1109/MMSP.2018.8547128 ]

Boulgouris N V and Chi Z X. 2007. Gait recognition using radon transform and linear discriminant analysis. IEEE Transactions on Image Processing, 16(3): 731-740[DOI:10.1109/TIP.2007.891157]

Boulkenafet Z, Komulainen J and Hadid A. 2016. Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, 11(8): 1818-1830[DOI:10.1109/TIFS.2016.2555286]

Bourlard H and Kamp Y. 1988. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics, 59(4): 291-294[DOI:10.1007/BF00332918]

Bowyer K, Czajka A, Flynn P, Moreira D and Scheirer W. 2021. ND. Notre Dame CVRL[EB/OL]. [2021-02-03] . https://cvrl.nd.edu/projects/data/ https://cvrl.nd.edu/projects/data/

Bub G, Tecza M, Helmes M, Lee P and Kohl P. 2010. Temporal pixel multiplexing for simultaneous high-speed, high-resolution imaging. Nature Methods, 7(3): 209-211[DOI:10.1038/nmeth.1429]

Bunda S T. 2018. 3D point cloud reconstruction based on the finger vascular pattern[EB/OL]. [2021-02-03] . http://essay.utwente.nl/75284/ http://essay.utwente.nl/75284/

Burgos-Artizzu X P, Perona P and Dollár P. 2013. Robust face landmark estimation under occlusion//Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE: 1513-1520[ DOI: 10.1109/ICCV.2013.191 http://dx.doi.org/10.1109/ICCV.2013.191 ]

Campbell J P. 1997. Speaker recognition: a tutorial. Proceedings of IEEE, 85(9): 1437-1462[DOI:10.1109/5.628714]

Cao K and Jain A K. 2015. Latent orientation field estimation via convolutional neural network//Proceedings of the 2015 International Conference on Biometrics (ICB). Phuket, Thailand: IEEE: 349-356[ DOI: 10.1109/ICB.2015.7139060 http://dx.doi.org/10.1109/ICB.2015.7139060 ]

Cao K and Jain A K. 2019. Automated latent fingerprint recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4): 788-800[DOI:10.1109/TPAMI.2018.2818162]

Cao Q, Shen L, Xie W D, Parkhi O M and Zisserman A. 2018. VGGFace2: a dataset for recognising faces across pose and age//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Xi'an, China: IEEE: 67-74[ DOI: 10.1109/FG.2018.00020 http://dx.doi.org/10.1109/FG.2018.00020 ]

Cao X D, Wei Y C, Wen F and Sun J. 2014. Face alignment by explicit shape regression. International Journal of Computer Vision, 107(2): 177-190[DOI:10.1007/s11263-013-0667-3]

Cao Z, Simon T, Wei S E and Sheikh Y. 2017. Realtime multi-person 2D pose estimation using part affinity fields//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 1302-1310[ DOI: 10.1109/CVPR.2017.143 http://dx.doi.org/10.1109/CVPR.2017.143 ]

Chalidabhongse T, Kruger V and Chellappa R. 2001. The UMD database for human identification at a distance[EB/OL]. [2021-02-03] . Technical Report, University of Maryland, USA. http://www.umiacs.umd.edu/labs/pirl/hid/data.html http://www.umiacs.umd.edu/labs/pirl/hid/data.html

Chao H Q, He Y W, Zhang J P and Feng J F. 2019. GaitSet: regarding gait as a set for cross-view gait recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1): 8126-8133[DOI:10.1609/aaai.v33i01.33018126]

Chen C H, Liang J M and Zhu X C. 2011. Gait recognition based on improved dynamic Bayesian networks. Pattern Recognition, 44(4): 988-995[DOI:10.1016/j.patcog.2010.10.021]

Chen C H, Liang J M, Zhao H, Hu H H and Tian J. 2009. Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition Letters, 30(11): 977-984[DOI:10.1016/j.patrec.2009.04.012]

Chen C J and Ross A. 2018. A multi-task convolutional neural network for joint iris detection and presentation attack detection//Proceedings of 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW). Lake Tahoe, USA: IEEE: 44-51[ DOI: 10.1109/WACVW.2018.00011 http://dx.doi.org/10.1109/WACVW.2018.00011 ]

Chen D P, Li H S, Xiao T, Yi S and Wang X G. 2018a. Video person Re-identification with competitive snippet-similarity aggregation and co-attentive snippet embedding//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 1169-1178[ DOI: 10.1109/CVPR.2018.00128 http://dx.doi.org/10.1109/CVPR.2018.00128 ]

Chen D, Cao X D, Wen F and Sun J. 2013. Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification//Proceedings of 2013 IEEE Conference on Computer Vision and Patter Recognition. Portland, USA: IEEE: 3025-3032[ DOI: 10.1109/CVPR.2013.389 http://dx.doi.org/10.1109/CVPR.2013.389 ]

Chen G Y, Lin C Z, Ren L L, Lu J W and Zhou J. 2019. Self-critical attention learning for person re-identification//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE: 9636-9645[ DOI: 10.1109/ICCV.2019.00973 http://dx.doi.org/10.1109/ICCV.2019.00973 ]

Chen L W, Guo W and Dai L R. 2010. Speaker verification against synthetic speech//Proceedings of the 7th International Symposium on Chinese Spoken Language Processing. Tainan, China: IEEE: 309-312[ DOI: 10.1109/ISCSLP.2010.5684887 http://dx.doi.org/10.1109/ISCSLP.2010.5684887 ]

Chen M and Denoyer L. 2017. Multi-view generative adversarial networks//Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Skopje, Macedonia: Springer: 175-188[ DOI: 10.1007/978-3-319-71246-8_11 http://dx.doi.org/10.1007/978-3-319-71246-8_11 ]

Chen S, Ren K, Piao S X, Wang C, Wang Q, Weng J, Su L and Mohaisen A. 2017. You can hear but you cannot steal: defending against voice impersonation attacks on smartphones//Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS). Atlanta, USA: IEEE: 183-195[ DOI: 10.1109/ICDCS.2017.133 http://dx.doi.org/10.1109/ICDCS.2017.133 ]

Chen T, Yin W T, Zhou X S, Comaniciu D and Huang T S. 2006. Total variation models for variable lighting face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9): 1519-1524[DOI:10.1109/TPAMI.2006.195]

Chen W X and McDuff D. 2018. DeepPhys: video-based physiological measurement using convolutional attention networks//Proceedings of European Conference on Computer Vision. Munich, Germany: Springer: 356-373[ DOI: 10.1007/978-3-030-01216-8_22 http://dx.doi.org/10.1007/978-3-030-01216-8_22 ]

Chen Y B, Zhu X T and Gong S G. 2018b. Deep association learning for unsupervised video person re-identification[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1808.07301.pdf https://arxiv.org/pdf/1808.07301.pdf

Chen Y C, Zhu X T, Zheng W S and Lai J H. 2018c. Person re-identification by camera correlation aware feature augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2): 392-408[DOI:10.1109/TPAMI.2017.2666805]

Chen Y, Dass S C and Jain A K. 2005. Localized iris image quality using 2-D wavelets//Zhang D and Jain A, eds. Advances in Biometrics. vol. 3832. Berlin, Heidelberg: Springer: 373-381[ DOI: 10.1007/11608288_50 http://dx.doi.org/10.1007/11608288_50 ]

Cheng D, Gong Y H, Zhou S P, Wang J J and Zheng N N. 2016. Person re-identification by multi-channel parts-based CNN with improved triplet loss function//Proceedings of 2016 IEEE International Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 1335-1344[ DOI: 10.1109/CVPR.2016.149 http://dx.doi.org/10.1109/CVPR.2016.149 ]

Cheng X L, Xu M X and Zheng T F. 2019. Replay detection using CQT-based modified group delay feature and ResNeWt network in ASVspoof 2019//Proceedings of 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). Lanzhou, China: IEEE: 540-545[ DOI: 10.1109/APSIPAASC47483.2019.9023158 http://dx.doi.org/10.1109/APSIPAASC47483.2019.9023158 ]

Chesney R and Citron D. 2019. Deepfakes and the new disinformation war: the coming age of post-truth geopolitics[EB/OL]. [2021-02-03] . . https://heinonline.org/HOL/LandingPage?handle=hein.journals/fora98&div=18 https://heinonline.org/HOL/LandingPage?handle=hein.journals/fora98&div=18

Chingovska I, Anjos A and Marcel S. 2012. On the effectiveness of local binary patterns in face anti-spoofing//Proceedings of 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG). Darmstadt, Germany: IEEE: 1-7

Choi Y, Choi M, Kim M, Ha J W, Kim S and Choo J. 2018. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 8789-8797[ DOI: 10.1109/CVPR.2018.00916 http://dx.doi.org/10.1109/CVPR.2018.00916 ]

Chollet F. 2017. Xception: deep learning with depthwise separable convolutions//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 1800-1807[ DOI: 10.1109/CVPR.2017.195 http://dx.doi.org/10.1109/CVPR.2017.195 ]

Chopra S, Hadsell R and LeCun Y. 2005. Learning a similarity metric discriminatively, with application to face verification//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE: 539-546[ DOI: 10.1109/CVPR.2005.202 http://dx.doi.org/10.1109/CVPR.2005.202 ]

Chugh T and Jain A K. 2021. Fingerprint spoof detector generalization. IEEE Transactions on Information Forensics and Security, 16: 42-55[DOI:10.1109/TIFS.2020.2990789]

Chugh T, Cao K and Jain A K. 2018. Fingerprint spoof buster: use of minutiae-centered patches. IEEE Transactions on Information Forensics and Security, 13(9): 2190-2202[DOI:10.1109/TIFS.2018.2812193]

Chung J S, Nagrani A and Zisserman A. 2018. VoxCeleb2: deep speaker recognition[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1806.05622.pdf https://arxiv.org/pdf/1806.05622.pdf

Connell J, Ratha N, Gentile J and Bolle R. 2013. Fake iris detection using structured light//Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, Canada: IEEE: 8692-8696[ DOI: 10.1109/ICASSP.2013.6639363 http://dx.doi.org/10.1109/ICASSP.2013.6639363 ]

Cootes T F, Edwards G J and Taylor C J. 2001. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6): 681-685[DOI:10.1109/34.927467]

Cootes T F, Taylor C J, Cooper D H and Graham J. 1995. Active shape models-their training and application. Computer Vision and Image Understanding, 61(1): 38-59[DOI:10.1006/cviu.1995.1004]

Cui Z, Feng J J and Zhou J. 2020. Dense registration and mosaicking of fingerprints by training an end-to-end network. IEEE Transactions on Information Forensics and Security, 16: 627-642[DOI:10.1109/TIFS.2020.3017926]

Cui Z, Feng J J, Li S H, Lu J W and Zhou J. 2018. 2-D phase demodulation for deformable fingerprint registration. IEEE Transactions on Information Forensics and Security, 13(12): 3153-3165[DOI:10.1109/TIFS.2018.2841849]

Cunado D, Nixon M S and Carter J N. 1997. Using gait as a biometric, via phase-weighted magnitude spectra//Proceedings of International Conference on Audio- and Video-based Biometric Person Authentication. Crans-Montana, Switzerland: Springer, 1206: 93-102[ DOI: 10.1007/BFb0015984 http://dx.doi.org/10.1007/BFb0015984 ]

Czajka A, Fang Z Y and Bowyer K. 2019. Iris presentation attack detection based on photometric stereo features//Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, USA: IEEE: 877-885[ DOI: 10.1109/WACV.2019.00098 http://dx.doi.org/10.1109/WACV.2019.00098 ]

Czajka A. 2015. Pupil dynamics for iris liveness detection. IEEE Transactions on Information Forensics and Security, 10(4): 726-735[DOI:10.1109/TIFS.2015.2398815]

Dabouei A, Soleymani S, Dawson J and Nasrabadi N. 2019. Fast geometrically-perturbed adversarial faces//Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, USA: IEEE: 1979-1988[ DOI: 10.1109/WACV.2019.00215 http://dx.doi.org/10.1109/WACV.2019.00215 ]

Dandawate Y H and Inamdar S R. 2015. Fusion based multimodal biometric cryptosystem//Proceedings of 2015 International Conference on Industrial Instrumentation and Control (ICIC). Pune, India: IEEE: 1484-1489[ DOI: 10.1109/ⅡC.2015.7150984 http://dx.doi.org/10.1109/ⅡC.2015.7150984 ]

Darwish S M. 2017. Design of adaptive biometric gait recognition algorithm with free walking directions. IET Biometrics, 6(2): 53-60[DOI:10.1049/iet-bmt.2015.0082]

Das P, McGrath J, Fang Z Y, Boyd A, Jang G, Mohammadi A, Purnapatra S, Yambay D, Marcel S, Trokielewicz M, Maciejewicz P, Bowyer K, Czajka A, Schuckers S, Tapia J, Gonzalez S, Fang M L, Damer N, Boutros F, Kuijper A, Sharma R, Chen C J and Ross A. 2020. Iris liveness detection competition (LivDet-Iris) - the 2020 edition//Proceedings of 2020 IEEE International Joint Conference on Biometrics (IJCB). Houston, USA: IEEE: 1-9[ DOI: 10.1109/IJCB48548.2020.9304941 http://dx.doi.org/10.1109/IJCB48548.2020.9304941 ]

Daugman J G. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11): 1148-1161[DOI:10.1109/34.244676]

Daugman J. 2004. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 21-30[DOI:10.1109/TCSVT.2003.818350]

Daugman J. 2007. New methods in iris recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(5): 1167-1175[DOI:10.1109/TSMCB.2007.903540]

de Haan G and Jeanne V. 2013. Robust pulse rate from chrominance-based rPPG. IEEE Transactions on Biomedical Engineering, 60(10): 2878-2886[DOI:10.1109/TBME.2013.2266196]

Deb D,Zhang J B and Jain A K. 2019. AdvFaces: adversarial face synthesis[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1908.05008.pdf https://arxiv.org/pdf/1908.05008.pdf

Dehak N, Dumouchel P and Kenny P. 2007. Modeling prosodic features with joint factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, 15(7): 2095-2103[DOI:10.1109/TASL.2007.902758]

Dehak N, Kenny P J, Dehak R, Dumouchel P and Ouellet P. 2011. Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, 19(4): 788-798[DOI:10.1109/TASL.2010.2064307]

Deng J K, Guo J, Ververas E, Kotsia I and Zafeiriou S. 2020. RetinaFace: single-shot multi-level face localisation in the wild//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 5202-5211[ DOI: 10.1109/CVPR42600.2020.00525 http://dx.doi.org/10.1109/CVPR42600.2020.00525 ]

Deng J K, Guo J, Xue N N and Zafeiriou S. 2019. ArcFace: additive angular margin loss for deep face recognition//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 4685-4694[ DOI: 10.1109/CVPR.2019.00482 http://dx.doi.org/10.1109/CVPR.2019.00482 ]

Deng M Q, Wang C, Cheng F J and Zeng W. 2017. Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recognition, 67: 186-200[DOI:10.1016/j.patcog.2017.02.014]

Deng W J, Zheng L, Ye Q X, Kang G L, Yang Y and Jiao J B. 2018. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 994-1003[ DOI: 10.1109/CVPR.2018.00110 http://dx.doi.org/10.1109/CVPR.2018.00110 ]

Deshpande P D, Tavildar A S, Dandwate Y H and Shah E. 2016. Fusion of dorsal palm vein and palm print modalities for higher security applications//Proceedings of 2016 Conference on Advances in Signal Processing (CASP). Pune, India: IEEE: 201-206[ DOI: 10.1109/CASP.2016.7746165 http://dx.doi.org/10.1109/CASP.2016.7746165 ]

Devi M. 2015. Secure crypto multimodal biometric system for the privacy protection of user identification. International Journal of Innovative Research in Computer and Communication Engineering, 2015, 2

Dollár P, Tu Z W and Belongie S. 2006. Supervised learning of edges and object boundaries//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE: 1964-1971[ DOI: 10.1109/CVPR.2006.298 http://dx.doi.org/10.1109/CVPR.2006.298 ]

Dollár P, Welinder P and Perona P. 2010. Cascaded pose regression//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE: 1078-1085[ DOI: 10.1109/CVPR.2010.5540094 http://dx.doi.org/10.1109/CVPR.2010.5540094 ]

Donahue J, Hendricks L A, Guadarrama S, Rohrbach M, Venugopalan S, Darrell T and Saenko K. 2015. Long-term recurrent convolutional networks for visual recognition and description//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE: 2625-2634[ DOI: 10.1109/CVPR.2015.7298878 http://dx.doi.org/10.1109/CVPR.2015.7298878 ]

Dong L Q, Du H Y, Liu M, Zhao Y J, Li X Y, Feng S J, Liu X H, Hui M, Kong L Q and Hao Q. 2019a. Extended-depth-of-field object detection with wavefront coding imaging system. Pattern Recognition Letters, 125: 597-603[DOI:10.1016/j.patrec.2019.06.011]

Dong W B, Sun Z N and Tan T N. 2011. Iris matching based on personalized weight map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9): 1744-1757[DOI:10.1109/TPAMI.2010.227]

Dong Y P, Su H, Wu B Y, Li Z F, Liu W, Zhang T and Zhu J. 2019b. Efficient decision-based black-box adversarial attacks on face recognition//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA: IEEE: 7706-7714[ DOI: 10.1109/CVPR.2019.00790 http://dx.doi.org/10.1109/CVPR.2019.00790 ]

El-Din Y S, Moustafa M N and Mahdi H. 2020. Deep convolutional neural networks for face and iris presentation attack detection: survey and case study. IET Biometrics, 9(5): 179-193[DOI:10.1049/iet-bmt.2020.0004]

Engelsma J J and Jain A K. 2019. Generalizing fingerprint spoof detector: learning a one-class classifier//Proceedings of 2019 International Conference on Biometrics (ICB). Crete, Greece: IEEE: 1-8[ DOI: 10.1109/ICB45273.2019.8987319 http://dx.doi.org/10.1109/ICB45273.2019.8987319 ]

Eom C and Ham B. 2019. Learning disentangled representation for robust person re-identification[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1910.12003.pdf https://arxiv.org/pdf/1910.12003.pdf

Fan C, Peng Y J, Cao C S, Liu X, Hou S H, Chi J N, Huang Y Z, Li Q and He Z Q. 2020a. GaitPart: temporal part-based model for gait recognition//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE: 14213-14221[ DOI: 10.1109/CVPR42600.2020.01423 http://dx.doi.org/10.1109/CVPR42600.2020.01423 ]

Fan L J, Li T H, Fang R Y, Hristov R, Yuan Y and Katabi D. 2020b. Learning longterm representations for person re-identification using radio signals//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 10696-10706[ DOI: 10.1109/CVPR42600.2020.01071 http://dx.doi.org/10.1109/CVPR42600.2020.01071 ]

Fan X, Jiang W, Luo H and Fei M J. 2019. SphereReID: deep hypersphere manifold embedding for person re-identification. Journal of Visual Communication and Image Representation, 60: 51-58[DOI:10.1016/j.jvcir.2019.01.010]

Fan Y, Kang J W, Li L T, Li K C, Chen H L, Cheng S T, Zhang P Y, Zhou Z Y, Cai Y Q and Wang D. 2020c. CN-Celeb: a challenging Chinese speaker recognition dataset//Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE: 7604-7608[ DOI: 10.1109/ICASSP40776.2020.9054017 http://dx.doi.org/10.1109/ICASSP40776.2020.9054017 ]

Fang Y X, Wu Q X and Kang W X. 2018. A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing, 290: 100-107[DOI:10.1016/j.neucom.2018.02.042]

Fang Z Y, Czajka A and Bowyer K W. 2021. Robust iris presentation attack detection fusing 2D and 3D information. IEEE Transactions on Information Forensics and Security, 16: 510-520[DOI:10.1109/TIFS.2020.3015547]

Farid H. 2009. Image forgery detection. IEEE Signal Processing Magazine, 26(2): 16-25[DOI:10.1109/MSP.2008.931079]

Feng J J, Zhou J and Jain A K. 2013. Orientation field estimation for latent fingerprint enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4): 925-940[DOI:10.1109/TPAMI.2012.155]

Feng Z H, Kittler J, Awais M, Huber P and Wu X J. 2018. Wing loss for robust facial landmark localisation with convolutional neural networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 2235-2245[ DOI: 10.1109/CVPR.2018.00238 http://dx.doi.org/10.1109/CVPR.2018.00238 ]

Ferrer M A, Morales A, Travieso C M and Alonso J B. 2009. Combining hand biometric traits for personal identification//Proceedings of the 43rd Annual 2009 International Carnahan Conference on Security Technology. Zurich, Switzerland: IEEE: 155-159[ DOI: 10.1109/CCST.2009.5335547 http://dx.doi.org/10.1109/CCST.2009.5335547 ]

Ferrer M A, Vargas F and Morales A. 2011. Bispectral contactless hand based biometric system//Proceedings of the CONATEL 2011. Arequipa, Peru: IEEE: 1-6[ DOI: 10.1109/CONATEL.2011.5958660 http://dx.doi.org/10.1109/CONATEL.2011.5958660 ]

Font R. 2019. A denoising autoencoder for speaker recognition. Results on the MCE 2018 challenge//Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, UK: IEEE: 6016-6020[ DOI: 10.1109/ICASSP.2019.8683525 http://dx.doi.org/10.1109/ICASSP.2019.8683525 ]

Fried O, Tewari A, Zollhöfer M, Finkelstein A, Shechtman E, Goldman D B, Genova K, Jin Z Y, Theobalt C and Agrawala M. 2019. Text-based editing of talking-head video. ACM Transactions on Graphics, 38(4): #68[DOI:10.1145/3306346.3323028]

Funahasahi T, Tominaga M, Fujiwara T and Koshimizu H. 2004. Hierarchical face tracking by using PTZ camera//Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Seoul, Korea (South): IEEE: 427-432[ DOI: 10.1109/AFGR.2004.1301570 http://dx.doi.org/10.1109/AFGR.2004.1301570 ]

Galbally J, Bostrom G and Beslay L. 2017. Full 3D touchless fingerprint recognition: sensor, database and baseline performance//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 225-233[ DOI: 10.1109/BTAS.2017.8272702 http://dx.doi.org/10.1109/BTAS.2017.8272702 ]

Gałka J, Grzywacz M and Samborski R. 2015. Playback attack detection for text-dependent speaker verification over telephone channels. Speech Communication, 67: 143-153[DOI:10.1016/j.specom.2014.12.003]

Gangwar A and Joshi A. 2016. DeepIrisNet: deep iris representation with applications in iris recognition and cross-sensor iris recognition//Proceedings of 2016 IEEE International Conference on Image Processing. Phoenix, USA: IEEE: 2301-2305[ DOI: 10.1109/ICIP.2016.7532769 http://dx.doi.org/10.1109/ICIP.2016.7532769 ]

Gao S, Wang J Y, Lu H C and Liu Z M. 2020. Pose-guided visible part matching for occluded person ReID//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 11741-11749[ DOI: 10.1109/CVPR42600.2020.01176 http://dx.doi.org/10.1109/CVPR42600.2020.01176 ]

Garcia D C and de Queiroz R L. 2015. Face-spoofing 2 d-detection based on moiré-pattern analysis. IEEE Transactions on Information Forensics and Security, 10(4): 778-786[DOI:10.1016/j.dsp.2020.102852]

Ge Y X, Zhu F, Chen D P, Zhao R and Li H S. 2020. Self-paced contrastive learning with hybrid memory for domain adaptive object Re-ID[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2006.02713v2.pdf https://arxiv.org/pdf/2006.02713v2.pdf

Georghiades A S, Belhumeur P N and Kriegman D J. 2001. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6): 643-660[DOI:10.1109/34.927464]

Gilani S Z and Mian A. 2018. Learning from millions of 3D scans for large-scale 3D face recognition//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 1896-1905[ DOI: 10.1109/CVPR.2018.00203 http://dx.doi.org/10.1109/CVPR.2018.00203 ]

Godfrey J J, Holliman E C and McDaniel J. 1992. SWITCHBOARD: telephone speech corpus for research and development//Proceedings of 1992 IEEE International Conference on Acoustics, Speech and Signal Processing. San Francisco, USA: IEEE: 517-520[ DOI: 10.1109/ICASSP.1992.225858 http://dx.doi.org/10.1109/ICASSP.1992.225858 ]

Goel A, Singh A, Agarwal A, Vatsa M and Singh R. 2018. SmartBox: benchmarking adversarial detection and mitigation algorithms for face recognition//Proceedings of the 9th IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS). Redondo Beach, USA: IEEE: 1-7[ DOI: 10.1109/BTAS.2018.8698567 http://dx.doi.org/10.1109/BTAS.2018.8698567 ]

Gong X Y, Chang S Y, Jiang Y F and Wang Z Y. 2019. AutoGAN: neural architecture search for generative adversarial networks//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE: 3223-3233[ DOI: 10.1109/ICCV.2019.00332 http://dx.doi.org/10.1109/ICCV.2019.00332 ]

Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, United States: MIT Press: 2672-2680

Gopal, Srivastava S, Bhardwaj S and Bhargava S. 2016. Fusion of palm-phalanges print with palmprint and dorsal hand vein. Applied Soft Computing, 47: 12-20[DOI:10.1016/j.asoc.2016.05.039]

Goswami G, Agarwal A, Ratha N, Singh R and Vatsa M. 2019. Detecting and mitigating adversarial perturbations for robust face recognition. International Journal of Computer Vision, 127(6/7): 719-742[DOI:10.1007/s11263-019-01160-w]

Gray D and Tao H. 2008. Viewpoint invariant pedestrian recognition with an ensemble of localized features//Proceedings of European Conference on Computer Vision. Marseille, France: Springer: 262-275[ DOI: 10.1007/978-3-540-88682-2_21 http://dx.doi.org/10.1007/978-3-540-88682-2_21 ]

Gross R and Shi J B. 2001. The CMU Motion of Body (MoBo) Database. Technical Report CMU-RI-TR-01-18. Carnegie Mellon University, USA

Gross R, Matthews I, Cohn J, Kanade T and Baker S. 2008. Multi-PIE//Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition. Amsterdam, the Netherlands: IEEE: 1-8[ DOI: 10.1109/AFGR.2008.4813399 http://dx.doi.org/10.1109/AFGR.2008.4813399 ]

Gu S, Feng J J, Lu J W and Zhou J. 2018. Efficient rectification of distorted fingerprints. IEEE Transactions on Information Forensics and Security, 13(1): 156-169[DOI:10.1109/TIFS.2017.2745685]

Gu S, Feng J J, Lu J W and Zhou J. 2021. Latent fingerprint registration via matching densely sampled points. IEEE Transactions on Information Forensics and Security, 16: 1231-1244[DOI:10.1109/TIFS.2020.3032041]

Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V and Courville A. 2017. Improved training of wasserstein GANs//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc. : 5769-5779[ DOI: 10.5555/3295222.3295327 http://dx.doi.org/10.5555/3295222.3295327 ]

Guo Y D, Zhang L, Hu Y X, He X D and Gao J F. 2016. MS-Celeb-1M: a dataset and benchmark for large-scale face recognition//Proceedings of European Conference on Computer Vision (ECCV). Amsterdam, the Netherlands: Springer: 87-102[ DOI: 10.1007/978-3-319-46487-9_6 http://dx.doi.org/10.1007/978-3-319-46487-9_6 ]

Guo Z H, Zhang D, Zhang L, Zuo W M and Lu G M. 2011. Empirical study of light source selection for palmprint recognition. Pattern Recognition Letters, 32(2): 120-126[DOI:10.1016/j.patrec.2010.09.026]

Gupta A, Malage A, More D, Hemane P, Bhautmage P and Dhandekar D. 2014. Feature level fusion of face, palm vein and palm print modalities using discrete cosine transform//Proceedings of 2014 International Conference on Advances in Engineering and Technology Research. Unnao, India: IEEE: 1-5[ DOI: 10.1109/ICAETR.2014.7012805 http://dx.doi.org/10.1109/ICAETR.2014.7012805 ]

Gupta P and Gupta P. 2018. Multibiometric authentication system using slap fingerprints, palm dorsal vein, and hand geometry. IEEE Transactions on Industrial Electronics, 65(12): 9777-9784[DOI:10.1109/TIE.2018.2823686]

Hammami M, Ben Jemaa S and Ben-Abdallah H. 2014. Selection of discriminative sub-regions for palmprint recognition. Multimedia Tools and Applications, 68(3): 1023-1050[DOI:10.1007/s11042-012-1109-x]

Han J and Bhanu B. 2006. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2): 316-322[DOI:10.1109/TPAMI.2006.38]

Hao Z A, Fang P Y and Yang H W. 2020. Finger vein recognition based on multi-task learning//Proceedings of the 5th International Conference on Mathematics and Artificial Intelligence. Chengdu, China: ACM: 133-140[ DOI: 10.1145/3395260.3395277 http://dx.doi.org/10.1145/3395260.3395277 ]

Hayfron-Acquah J B, Nixon M S and Carter J N. 2003. Automatic gait recognition by symmetry analysis. Pattern Recognition Letters, 24(13): 2175-2183[DOI:10.1016/S0167-8655(03)00086-2]

He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]

He L X, Liang J, Li H Q and Sun Z N. 2018. Deep spatial feature reconstruction for partial person re-identification: alignment-free approach//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7073-7082[ DOI: 10.1109/CVPR.2018.00739 http://dx.doi.org/10.1109/CVPR.2018.00739 ]

He L X, Wang Y G, Liu W, Zhao H, Sun Z N and Feng J S. 2019a. Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE: 8449-8458[ DOI: 10.1109/ICCV.2019.00854 http://dx.doi.org/10.1109/ICCV.2019.00854 ]

He Z F, Sun Z N, Tan T N, Qiu X C, Zhong C and Dong W B. 2008. Boosting ordinal features for accurate and fast iris recognition//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE: 1-8[ DOI: 10.1109/CVPR.2008.4587645 http://dx.doi.org/10.1109/CVPR.2008.4587645 ]

He Z F, Tan T N, Sun Z N and Qiu X C. 2009. Toward accurate and fast iris segmentation for iris biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9): 1670-1684[DOI:10.1109/TPAMI.2008.183]

He Z L, Zuo W M, Kan M N, Shan S G and Chen X L. 2019b. AttGAN: facial attribute editing by only changing what you want. IEEE Transactions on Image Processing, 28(11): 5464-5478[DOI:10.1109/TIP.2019.2916751]

Heigold G, Moreno I, Bengio S and Shazeer N. 2016. End-to-end text-dependent speaker verification//Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Shanghai, China: IEEE: 5115-5119[ DOI: 10.1109/ICASSP.2016.7472652 http://dx.doi.org/10.1109/ICASSP.2016.7472652 ]

Hermans A, Beyer L and Leibe B. 2017. In defense of the triplet loss for person re-identification[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1703.07737v4.pdf https://arxiv.org/pdf/1703.07737v4.pdf

Hinton G E and Zemel R S. 1993. Autoencoders, minimum description length and Helmholtz free energy//Proceedings of the 6th International Conference on Neural Information Processing Systems. San Francisco, USA: Morgan Kaufmann Publishers Inc. : 3-10

Hofbauer H, Jalilian E and Uhl A. 2019. Exploiting superior CNN-based iris segmentation for better recognition accuracy. Pattern Recognition Letters, 120: 17-23[DOI:10.1016/j.patrec.2018.12.021]

Hollingsworth K P, Bowyer K W and Flynn P J. 2009. The best bits in an iris code. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6): 964-973[DOI:10.1109/TPAMI.2008.185]

Hossain M A, Makihara Y, Wang J Q and Yagi Y. 2010. Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recognition, 43(6): 2281-2291[DOI:10.1016/j.patcog.2009.12.020]

Hou R B, Ma B P, Chang H, Gu X Q, Shan S G and Chen X L. 2019. VRSTC: occlusion-free video person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA: IEEE: 7176-7185[ DOI: 10.1109/CVPR.2019.00735 http://dx.doi.org/10.1109/CVPR.2019.00735 ]

Hou S H, Cao C S, Liu X and Huang Y Z. 2020. Gait lateral network: learning discriminative and compact representations for gait recognition//Proceedings of European Conference on Computer Vision. Glasgow, UK: Springer: 382-398[ DOI: 10.1007/978-3-030-58545-7_22 http://dx.doi.org/10.1007/978-3-030-58545-7_22 ]

Hsu C B, Lee J C, Chuang S J and Kuei P Y. 2015. Gaussian directional pattern for dorsal hand vein recognition. The Imaging Science Journal, 63(1): 54-62[DOI:10.1179/1743131X14Y.0000000070]

Hsu C C, Lee C Y and Zhuang Y X. 2018. Learning to detect fake face images in the wild//Proceedings of 2018 International Symposium on Computer, Consumer and Control (IS3C). Taichung, China: IEEE: 388-391[ DOI: 10.1109/IS3C.2018.00104 http://dx.doi.org/10.1109/IS3C.2018.00104 ]

Hsu C C, Zhuang Y X and Lee C Y. 2020. Deep fake image detection based on pairwise learning. Applied Sciences, 10(1): #370[DOI:10.3390/app10010370]

Hu J X, Zhang H, Xiao L H, Liu J, Li X G, He Z F and Li L. 2019. Seg-edge bilateral constraint network for iris segmentation//Proceedings of 2019 International Conference on Biometrics (ICB). Crete, Greece: IEEE: 1-6[ DOI: 10.1109/ICB45273.2019.8987405 http://dx.doi.org/10.1109/ICB45273.2019.8987405 ]

Hu P Y and Ramanan D. 2017. Finding tiny faces//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 1522-1530[ DOI: 10.1109/CVPR.2017.166 http://dx.doi.org/10.1109/CVPR.2017.166 ]

Hu Y, Sirlantzis K and Howells G. 2016. Iris liveness detection using regional features. Pattern Recognition Letters, 82: 242-250[DOI:10.1016/j.patrec.2015.10.010]

Huang B N, Dai Y G, Li R F, Tang D R and Li W X. 2010. Finger-vein authentication based on wide line detector and pattern normalization//Proceedings of 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE: 1269-1272[ DOI: 10.1109/ICPR.2010.316 http://dx.doi.org/10.1109/ICPR.2010.316 ]

Huang D S, Jia W and Zhang D. 2008. Palmprint verification based on principal lines. Pattern Recognition, 41(4): 1316-1328[DOI:10.1016/j.patcog.2007.08.016]

Huang G B, Ramesh M, Berg T and Learned-Miller E. 2007. Labeled Faces in the Wild: a Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts

Huang G, Liu Z, van der Maaten L and Weinberger K Q. 2017a. Densely connected convolutional networks//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 2261-2269[ DOI: 10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]

Huang R, Zhang S, Li T Y and He R. 2017b. Beyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE: 2458-2467[ DOI: 10.1109/ICCV.2017.267 http://dx.doi.org/10.1109/ICCV.2017.267 ]

Huang Y, Xu J S, Wu Q, Zheng Z D, Zhang Z X and Zhang J. 2019. Multi-pseudo regularized label for generated data in person re-identification. IEEE Transactions on Image Processing, 28(3): 1391-1403[DOI:10.1109/TIP.2018.2874715]

Huang Z X, Liu Y G, Wang X M and Hu J R. 2015. Study on sparse representation based classification for biometric verification[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1502.06073.pdf https://arxiv.org/pdf/1502.06073.pdf

Huang Z Z, Feng Z H, Kittler J and Liu Y G. 2018. Improve the spoofing resistance of multimodal verification with representation-based measures//Proceedings of Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Guangzhou, China: Springer: 388-399[ DOI: 10.1007/978-3-030-03338-5_33 http://dx.doi.org/10.1007/978-3-030-03338-5_33 ]

Huber P J. 1981. Robust Statistics. New York: John Wiley and Sons

Huh M, Liu A, Owens A and Efros A A. 2018. Fighting fake news: image splice detection via learned self-consistency//Proceedings of European Conference on Computer Vision (ECCV). Munich, Germany: Springer: 106-124[ DOI: 10.1007/978-3-030-01252-6_7 http://dx.doi.org/10.1007/978-3-030-01252-6_7 ]

Isola P, Zhu J Y, Zhou T H and Efros A A. 2017. Image-to-image translation with conditional adversarial networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 5967-5976[ DOI: 10.1109/CVPR.2017.632 http://dx.doi.org/10.1109/CVPR.2017.632 ]

Iwama H, Okumura M, Makihara Y and Yagi Y. 2012. The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Transactions on Information Forensics and Security, 7(5): 1511-1521[DOI:10.1109/TIFS.2012.2204253]

Jain A K and Feng J J. 2011. Latent fingerprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1): 88-100[DOI:10.1109/TPAMI.2010.59]

Jain A K and Ross A A. 2004. Multibiometric systems. Communications of the ACM, 47(1): 34-40[DOI:10.1145/962081.962102]

Jain V and Learned-Miller E. 2010. FDDB: a Benchmark for Face Detection in Unconstrained Settings. UMass Amherst Technical Report

Jain V, Bollmann B, Richardson M, Berger D R, Helmstaedter M N, Briggman K L, Denk W, Bowden J B, Mendenhall J M, Abraham W C, Harris K M, Kasthuri N, Hayworth K J, Schalek R, Tapia J C, Lichtman J W and Seung H S. 2010. Boundary learning by optimization with topological constraints//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE: 2488-2495[ DOI: 10.1109/CVPR.2010.5539950 http://dx.doi.org/10.1109/CVPR.2010.5539950 ]

Jalalifar S A, Hasani H and Aghajan H. 2018. Speech-driven facial reenactment using conditional generative adversarial networks[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1803.07461.pdf https://arxiv.org/pdf/1803.07461.pdf

Jalilian E and Uhl A. 2017. Iris segmentation using fully convolutional encoder-decoder networks//Bhanu B and Kumar A, eds. Deep Learning for Biometrics. [s. l.]: Springer: 133-155[ DOI: 10.1007/978-3-319-61657-5_6 http://dx.doi.org/10.1007/978-3-319-61657-5_6 ]

Jalilian E, Karakaya M and Uhl A. 2020. End-to-end off-angle iris recognition using CNN based iris segmentation//Proceedings of 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). Darmstadt, Germany: IEEE: 1-7

Jenadeleh M, Pedersen M and Saupe D. 2018. Realtime quality assessment of iris biometrics under visible light//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE: 556-565[ DOI: 10.1109/CVPRW.2018.00085 http://dx.doi.org/10.1109/CVPRW.2018.00085 ]

Jha R R, Jaswal G, Gupta D, Saini S and Nigam A. 2020. PixiSegNet: pixel-level iris segmentation network using convolutional encoder-decoder with stacked hourglass bottleneck. IET Biometrics, 9(1): 11-24[DOI:10.1049/iet-bmt.2019.0025]

Ji S W, Xu W, Yang M and Yu K. 2013. 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1): 221-231[DOI:10.1109/TPAMI.2012.59]

Jia W, Hu R X, Lei Y K, Zhao Y and Gui J. 2014. Histogram of oriented lines for palmprint recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(3): 385-395[DOI:10.1109/TSMC.2013.2258010]

Jia Y P, Zhang J, Shan S G and Chen X L. 2020. Single-side domain generalization for face anti-spoofing//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 8481-8490[ DOI: 10.1109/CVPR42600.2020.00851 http://dx.doi.org/10.1109/CVPR42600.2020.00851 ]

Jiang S Y, Tao Z Q and Fu Y. 2019. Segmentation guided image-to-image translation with adversarial networks//Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition. Lille, France: IEEE: 1-7[ DOI: 10.1109/FG.2019.8756542 http://dx.doi.org/10.1109/FG.2019.8756542 ]

Jo Y and Park J. 2019. SC-FEGAN: face editing generative adversarial network with user's sketch and color//Proceedings of 2019 IEEE/CVFInternational Conference on Computer Vision. Seoul, Korea (South): IEEE: 1745-1753[ DOI: 10.1109/ICCV.2019.00183 http://dx.doi.org/10.1109/ICCV.2019.00183 ]

Joseph R. 1985. Apparatus for the identification of individuals. US, 19850795341

Juels A and Sudan M. 2006. A fuzzy vault scheme. Designs, Codes and Cryptography, 38(2): 237-257[DOI:10.1007/s10623-005-6343-z]

Kabaciński R and Kowalski M. 2011. Vein pattern database and benchmark results. Electronics Letters, 47(20): 1127-1128[DOI:10.1049/el.2011.1441]

Kalayeh M M, Basaran E, Gökmen M, Kamasak M E and Shah M. 2018. Human semantic parsing for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 1062-1071[ DOI: 10.1109/CVPR.2018.00117 http://dx.doi.org/10.1109/CVPR.2018.00117 ]

Kale A, Rajagopalan A N, Cuntoor N and Kruger V. 2002. Gait-based recognition of humans using continuous HMMs//Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, USA: IEEE: 336-341[ DOI: 10.1109/AFGR.2002.1004176 http://dx.doi.org/10.1109/AFGR.2002.1004176 ]

Kale A, Sundaresan A, Rajagopalan A N, Cuntoor N P, Roy-Chowdhury A K, Kruger V and Chellappa R. 2004. Identification of humans using gait. IEEE Transactions on Image Processing, 13(9): 1163-1173[DOI:10.1109/TIP.2004.832865]

Kalka N D, Zuo J Y, Schmid N A and Cukic B. 2010. Estimating and fusing quality factors for iris biometric images. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 40(3): 509-524[DOI:10.1109/TSMCA.2010.2041658]

Kamble M and Patil H. 2018. Novel variable length energy separation algorithm using instantaneous amplitude features for replay detection//Proceedings of the INTERSPEECH 2018. Hyderabad, India: [s. n.]: 646-650[ DOI: 10.21437/Interspeech.2018-1687 http://dx.doi.org/10.21437/Interspeech.2018-1687 ]

Kaneko T, Kameoka H, Tanaka K and Hojo N. 2019. Cyclegan-VC2: improved cyclegan-based non-parallel voice conversion//Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, UK: IEEE: 6820-6824[ DOI: 10.1109/ICASSP.2019.8682897 http://dx.doi.org/10.1109/ICASSP.2019.8682897 ]

Kang W H, Mun S H, Han M H and Kim N S. 2020a. Disentangled speaker and nuisance attribute embedding for robust speaker verification. IEEE Access, 8: 141838-141849[DOI:10.1109/ACCESS.2020.3012893]

Kang W X and Wu Q X. 2014. Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Transactions on Information Forensics and Security, 9(11): 1974-1985[DOI:10.1109/TIFS.2014.2361020]

Kang W X, Liu H D, Luo W and Deng F Q. 2020b. Study of a full-view 3D finger vein verification technique. IEEE Transactions on Information Forensics and Security, 15: 1175-1189[DOI:10.1109/TIFS.2019.2928507]

Kang W X, Lu Y T, Li D J and Jia W. 2019. From noise to feature: exploiting intensity distribution as a novel soft biometric trait for finger vein recognition. IEEE Transactions on Information Forensics and Security, 14(4): 858-869[DOI:10.1109/TIFS.2018.2866330]

Kang W X. 2012. Vein pattern extraction based on vectorgrams of maximal intra-neighbor difference. Pattern Recognition Letters, 33(14): 1916-1923[DOI:10.1016/j.patrec.2012.02.020]

Karras T, Aila T, Laine S and Lehtinen J. 2017. Progressive growing of GANs for improved quality, stability, and variation[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1710.10196.pdf https://arxiv.org/pdf/1710.10196.pdf

Kauba C and Uhl A. 2018. Shedding light on the veins-reflected light or transillumination in hand-vein recognition//Proceedings of 2018 International Conference on Biometrics (ICB). Gold Coast, Australia: IEEE: 283-290[ DOI: 10.1109/ICB2018.2018.00050 http://dx.doi.org/10.1109/ICB2018.2018.00050 ]

Kemelmacher-Shlizerman I, Seitz S M, Miller D and Brossard E. 2016. The MegaFace benchmark: 1 million faces for recognition at scale//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 4873-4882[ DOI: 10.1109/CVPR.2016.527 http://dx.doi.org/10.1109/CVPR.2016.527 ]

Kim D, Hernandez M, Choi J and Medioni G. 2017. Deep 3D face identification//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver, USA: IEEE: 133-142[ DOI: 10.1109/BTAS.2017.8272691 http://dx.doi.org/10.1109/BTAS.2017.8272691 ]

Kisku D R, Gupta P, Mehrotra H and Sing J K. 2009. Multimodal belief fusion for face and ear biometrics. Intelligent Information Management, 1: 166-171[DOI:10.4236/iim.2009.13024]

Klare B F, Klein B, Taborsky E, Blanton A, Cheney J, Allen K, Grother P, Mah A, Burge M and Jain A K. 2015. Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus benchmark A//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE: 1931-1939[ DOI: 10.1109/CVPR.2015.7298803 http://dx.doi.org/10.1109/CVPR.2015.7298803 ]

Kollreider K, Fronthaler H, Faraj M I and Bigun J. 2007. Real-time face detection and motion analysis with application in "liveness" assessment. IEEE Transactions on Information Forensics and Security, 2(3): 548-558[DOI:10.1109/TIFS.2007.902037]

Komogortsev O V, Karpov A and Holland C D. 2015. Attack of mechanical replicas: liveness detection with eye movements. IEEE Transactions on Information Forensics and Security, 10(4): 716-725[DOI:10.1109/TIFS.2015.2405345]

Kong A W K and Zhang D. 2004. Competitive coding scheme for palmprint verification//Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE: 520-523[ DOI: 10.1109/ICPR.2004.1334184 http://dx.doi.org/10.1109/ICPR.2004.1334184 ]

Kono M, Ueki H and Umemura S. 2000. A new method for the identification of individuals by using of vein pattern matching of a finger//The 5th Symposium on Pattern Measurement. Yamaguchi, Japan: [s. n.]: 9-12

Köstinger M, Hirzer M, Wohlhart P, Roth P M and Bischof H. 2012. Large scale metric learning from equivalence constraints//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE: 2288-2295[ DOI: 10.1109/CVPR.2012.6247939 http://dx.doi.org/10.1109/CVPR.2012.6247939 ]

Köstinger M, Wohlhart P, Roth P M and Bischof H. 2011. Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization//Proceedings of 2011 IEEE International Conference on Computer Vision Workshops (ICCVW). Barcelona, Spain: IEEE: 2144-2151[ DOI: 10.1109/ICCVW.2011.6130513 http://dx.doi.org/10.1109/ICCVW.2011.6130513 ]

Krizhevsky A, Sutskever I and Hinton G E. 2012. Imagenet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc. : 1097-1105

Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90[DOI:10.1145/3065386]

Kumar A and Prathyusha K V. 2009. Personal authentication using hand vein triangulation and knuckle shape. IEEE Transactions on Image Processing, 18(9): 2127-2136[DOI:10.1109/TIP.2009.2023153]

Kumar A and Shekhar S. 2011. Personal identification using multibiometrics rank-level fusion. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(5): 743-752[DOI:10.1109/TSMCC.2010.2089516]

Kumar A and Zhang D. 2005. Biometric recognition using feature selection and combination//Proceedings of International Conference on Audio-and Video-Based Biometric Person Authentication. Rye Brook, USA: Springer: 813-822[ DOI: 10.1007/11527923_85 http://dx.doi.org/10.1007/11527923_85 ]

Kumar A and Zhou Y B. 2012. Human identification using finger images. IEEE Transactions on Image Processing, 21(4): 2228-2244[DOI:10.1109/TIP.2011.2171697]

Kumar R, Sotelo J, Kumar K, de Brébisson A and Bengio Y. 2017. ObamaNet: photo-realistic lip-sync from text[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1801.01442.pdf https://arxiv.org/pdf/1801.01442.pdf

Kurakin A, GoodfellowI J and Bengio S. 2017. Adversarial examples in the physical world[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1607.02533.pdf https://arxiv.org/pdf/1607.02533.pdf

Kurnianggoro L and Jo K H. 2019. Ensemble of predictions from augmented input as adversarial defense for face verification system//Proceedings of Asian Conference on Intelligent Information and Database Systems. Yogyakarta, Indonesia: Springer: 658-669[ DOI: 10.1007/978-3-030-14802-7_57 http://dx.doi.org/10.1007/978-3-030-14802-7_57 ]

Kuzu R S, Piciucco E, Maiorana E and Campisi P. 2020. On-the-fly finger-vein-based biometric recognition using deep neural networks. IEEE Transactions on Information Forensics and Security, 15: 2641-2654[DOI:10.1109/TIFS.2020.2971144]

Labati R D, Genovese A, Piuri V and Scotti F. 2016. Toward unconstrained fingerprint recognition: a fully touchless 3-D system based on two views on the move. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(2): 202-219[DOI:10.1109/TSMC.2015.2423252]

Lam T H W and Lee R S T. 2006. A new representation for human gait recognition: motion silhouettes image (MSI)//Proceedings of International Conference on Biometrics. Hong Kong, China: Springer: 612-618[ DOI: 10.1007/11608288_81 http://dx.doi.org/10.1007/11608288_81 ]

Lam T H W, Cheung K H and Liu J N K. 2011. Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognition, 44(4): 973-987[DOI:10.1016/j.patcog.2010.10.011]

Lass N J. 2012. Contemporary Issues in Experimental Phonetics. New York: Academic Press

Lavrentyeva G, Novoselov S, Malykh E, Kozlov A, Kudashev O and Shchemelinin V. 2017. Audio replay attack detection with deep learning frameworks//Proceedings of the INTERSPEECH 2017. Stockholm, Sweden: [s. n.]: 82-86[ DOI: 10.21437/Interspeech.2017-360 http://dx.doi.org/10.21437/Interspeech.2017-360 ]

LeCun Y and Fogelman-Soulié F. 1987. Modèles connexionnistes de l'apprentissage. Intellectica, 2(1): 114-143[DOI:10.3406/intel.1987.1804]

Lee E C and Park K R. 2009. Restoration method of skin scattering blurred vein image for finger vein recognition. Electronics Letters, 45(21): 1074-1076[DOI:10.1049/el.2009.1231]

Lee H C, Kang B J, Lee E C and Park K R. 2010. Finger vein recognition using weighted local binary pattern code based on a support vector machine. Journal of Zhejiang University Science C, 11(7): 514-524[DOI:10.1631/jzus.C0910550]

Lee J C, Lo T M and Chang C P. 2014. Dorsal hand vein recognition based on directional filter bank. Signal, Image and Video Processing, 10(1): 145-152[DOI:10.1007/s11760-014-0714-8]

Lee K A, Wang Q Q and Koshinaka T. 2019. The CORAL+ algorithm for unsupervised domain adaptation of PLDA//Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Brighton, UK: IEEE: 5821-5825[ DOI: 10.1109/ICASSP.2019.8682852 http://dx.doi.org/10.1109/ICASSP.2019.8682852 ]

Lee L and Grimson W E L. 2002. Gait analysis for recognition and classification//Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, USA: IEEE: 155-162[ DOI: 10.1109/AFGR.2002.1004148 http://dx.doi.org/10.1109/AFGR.2002.1004148 ]

Lee Y, Chen J C, Tseng C W and Lai S H. 2016. Accurate and robust face recognition from RGB-D images with a deep learning approach//Proceedings of the British Machine Vision Conference. UK: BMVA Press: #123[ DOI: 10.5244/C.30.123 http://dx.doi.org/10.5244/C.30.123 ]

Leon P L, Stewart B and Yamagishi J. 2012. Synthetic speech discrimination using pitch pattern statistics derived from image analysis//Proceedings of the 13th Annual Conference of the International Speech Communication Association. Portland, USA: [s. n.]: 370-373

Lewandowska M, Rumiński J, Kocejko T and Nowak J. 2011. Measuring pulse rate with a webcam-a non-contact method for evaluating cardiac activity//Proceedings of 2011 Federated Conference on Computer Science and Information Systems. Szczecin, Poland: IEEE: 405-410

Li C, Ma X K, Jiang B, Li X G, Zhang X W, Liu X, Cao Y, Kannan A and Zhu Z Y. 2017a. Deep speaker: an end-to-end neural speaker embedding system[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1705.02304 https://arxiv.org/pdf/1705.02304

Li D W, Chen X T, Zhang Z and Huang K Q. 2017b. Learning deep context-aware features overbody and latent parts for person re-identification//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 7398-7407[ DOI: 10.1109/CVPR.2017.782 http://dx.doi.org/10.1109/CVPR.2017.782 ]

Li J, Wang Y B, Wang C, Tai Y, Qian J J, Yang J, Wang C J, Li J L and Huang F Y. 2019a. DSFD: dual shot face detector//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 5055-5064[ DOI: 10.1109/CVPR.2019.00520 http://dx.doi.org/10.1109/CVPR.2019.00520 ]

Li L T, Liu R Q, Kang J W, Fan Y, Cui H, Cai Y Q, Vipperla R, Zheng T F and Wang D. 2020a. CN-Celeb: multi-genre speaker recognition[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2012.12468 https://arxiv.org/pdf/2012.12468

Li L T, Wang D, Zhang C H and Zheng T F. 2016a. Improving short utterance speaker recognition by modeling speech unit classes. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(6): 1129-1139[DOI:10.1109/TASLP.2016.2544660]

Li L Z, Bao J M, Yang H, Chen D and Wen F. 2019b. FaceShifter: towards high fidelity and occlusion aware face swapping[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1912.13457.pdf https://arxiv.org/pdf/1912.13457.pdf

Li M X, Zhu X T and Gong S G. 2018a. Unsupervised person re-identification by deep learning tracklet association//Proceedings of European Conference on Computer Vision. Munich, Germany: Springer: 772-788[ DOI: 10.1007/978-3-030-01225-0_45 http://dx.doi.org/10.1007/978-3-030-01225-0_45 ]

Li M X, Zhu X T and Gong S G. 2020b. Unsupervised tracklet person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(7): 1770-1782[DOI:10.1109/TPAMI.2019.2903058]

Li Q, Li X, Guo Z H and You J. 2015. Online personal verification by palmvein image through palmprint-like and palmvein information. Neurocomputing, 147: 364-371[DOI:10.1016/j.neucom.2014.06.050]

Li W, Zhu X T and Gong S G. 2018b. Harmonious attention network for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 2285-2294[ DOI: 10.1109/CVPR.2018.00243 http://dx.doi.org/10.1109/CVPR.2018.00243 ]

Li X B, Alikhani I, Shi J G, Seppanen T, Junttila J, Majamaa-Voltti K, Tulppo M and Zhao G Y. 2018c. The OBF database: a large face video database for remote physiological signal measurement and atrial fibrillation detection//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Xi'an, China: IEEE: 242-249[ DOI: 10.1109/FG.2018.00043 http://dx.doi.org/10.1109/FG.2018.00043 ]

Li X B, Han H, Lu H, Niu X S, Yu Z T, Dantcheva A, Zhao G Y and Shan S G. 2020c. The 1st challenge on remote physiological signal sensing (RePSS)//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, USA: IEEE: 1274-1281[ DOI: 10.1109/CVPRW50498.2020.00165 http://dx.doi.org/10.1109/CVPRW50498.2020.00165 ]

Li X X, Huang D and Wang Y H. 2016b. Comparative study of deep learning methods on dorsal hand vein recognition//Proceedings of Chinese Conference on Biometric Recognition. Chengdu, China: Springer: 296-306[ DOI: 10.1007/978-3-319-46654-5_33 http://dx.doi.org/10.1007/978-3-319-46654-5_33 ]

Li X, Li N, Zhong J H, Wu X X, Liu X Y, Su D, Yu D and Meng H L. 2020d. Investigating robustness of adversarial samples detection for automatic speaker verification[EB/OL][2020-10-12] . https://arxiv.org/pdf/2006.06186.pdf https://arxiv.org/pdf/2006.06186.pdf

Li X, Wu A C and Zheng W S. 2018d. Adversarial open-world person re-identification//Proceedings of the European Conference on Computer Vision. Munich, Germany: Springer: 287-303[ DOI: 10.1007/978-3-030-01216-8_18 http://dx.doi.org/10.1007/978-3-030-01216-8_18 ]

Li X, Zhong J H, Wu X X, Yu J W, Liu X Y and Meng H L. 2020e. Adversarial attacks on GMM I-vector based speaker verification systems//Proceedings of 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Barcelona, Spain: IEEE: 6579-6583[ DOI: 10.1109/ICASSP40776.2020.9053076 http://dx.doi.org/10.1109/ICASSP40776.2020.9053076 ]

Li Y J, Chen Y C, Lin Y Y, Du X F and Wang Y C F. 2019c. Recover and identify: a generative dual model for cross-resolution person re-identification//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 8089-8098[ DOI: 10.1109/ICCV.2019.00818 http://dx.doi.org/10.1109/ICCV.2019.00818 ]

Li Y J, Luo Z Y, Weng X S and Kitani K M. 2020f. Learning shape representations for clothing variations in person re-identification[EB/OL][2021-02-03] . https://arxiv.org/pdf/2003.07340v1.pdf https://arxiv.org/pdf/2003.07340v1.pdf

Li Y Z, Chang M C and Lyu S W. 2018e. In Ictu oculi: exposing AI created fake videos by detecting eye blinking//Proceedings of 2018 IEEE International Workshop on Information Forensics and Security (WIFS). Hong Kong, China: IEEE: 1-7[ DOI: 10.1109/WIFS.2018.8630787 http://dx.doi.org/10.1109/WIFS.2018.8630787 ]

Lian S, Luo Z, Zhong Z, Lin X, Su S and Li S. 2018. Attention guided u-net for accurate iris segmentation. Journal of Visual Communication and Image Representation, 56: 296-304[DOI:10.1016/j.jvcir.2018.10.001]

Liang W Q, Wang G C, Lai J H and Zhu J Y. 2018. M2 m-Gan: many-to-many generative adversarial transfer learning for person re-identification[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1811.03768.pdf https://arxiv.org/pdf/1811.03768.pdf

Liao R J, Cao C S, Garcia E B, Yu S Q and Huang Y Z. 2017. Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations//Proceedings Chinese Conference on Biometric Recognition. Beijing, China: Springer: 474-483[ DOI: 10.1007/978-3-319-69923-3_51 http://dx.doi.org/10.1007/978-3-319-69923-3_51 ]

Liao R J, Yu S Q, An W Z and Huang Y Z. 2020. A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition, 98: #107069[DOI:10.1016/j.patcog.2019.107069]

Liao S C, Zhao G Y, Kellokumpu V, Pietikäinen M and Li S Z. 2010. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE: 1301-1306[ DOI: 10.1109/CVPR.2010.5539817 http://dx.doi.org/10.1109/CVPR.2010.5539817 ]

Lin C H and Kumar A. 2017. Tetrahedron based fast 3D fingerprint identification using colored LEDs illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12): 3022-3033[DOI:10.1109/TPAMI.2017.2771292]

Lin C L and Fan K C. 2004. Biometric verification using thermal images of palm-dorsa vein patterns. IEEE Transactions on Circuits and Systems for Video Technology, 14(2): 199-213[DOI:10.1109/TCSVT.2003.821975]

Lin C L and Wang K J. 2010. A behavior classification based on enhanced gait energy image//Proceedings of 2010 International Conference on Networking and Digital Society. Wenzhou, China: IEEE: 589-592[ DOI: 10.1109/ICNDS.2010.5479416 http://dx.doi.org/10.1109/ICNDS.2010.5479416 ]

Lin C L, Fan K C and Chou F D. 2004. A novel approach to palmprint verification utilizing finger-webs as datum points. Image and Vision Computing, (3)

Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2017. Focal loss for dense object detection//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2999-3007[ DOI: 10.1109/ICCV.2017.324 http://dx.doi.org/10.1109/ICCV.2017.324 ]

Lin Y T, Xie L X, Wu Y, Yan C G and Tian Q. 2020. Unsupervised person re-identification via softened similarity learning//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3387-3396[ DOI: 10.1109/CVPR42600.2020.00345 http://dx.doi.org/10.1109/CVPR42600.2020.00345 ]

Lin Y T, Zheng L, Zheng Z D, Wu Y, Hu Z L, Yan C G and Yang Y. 2019. Improving person re-identification by attribute and identity learning. Pattern Recognition, 95: 151-161[DOI:10.1016/j.patcog.2019.06.006]

Liu B Y, Deng W H, Zhong Y Y, Wang M, Hu J N, Tao X Q and Huang Y H. 2019a. Fair loss: margin-aware reinforcement learning for deep face recognition//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE: 10051-10060[ DOI: 10.1109/ICCV.2019.01015 http://dx.doi.org/10.1109/ICCV.2019.01015 ]

Liu C J and Wechsler H. 2002. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing, 11(4): 467-476[DOI:10.1109/TIP.2002.999679]

Liu D and Sun D M. 2016. Contactless palmprint recognition based on convolutional neural network//Proceedings of the 13th IEEE International Conference on Signal Processing (ICSP). Chengdu, China: IEEE: 1363-1367[ DOI: 10.1109/ICSP.2016.7878049 http://dx.doi.org/10.1109/ICSP.2016.7878049 ]

Liu F and Zhang D. 2014. 3D fingerprint reconstruction system using feature correspondences and prior estimated finger model. Pattern Recognition, 47(1): 178-193[DOI:10.1016/j.patcog.2013.06.009]

Liu F, Yin Y L, Yang G P, Dong L M and Xi X M. 2014. Finger vein recognition with superpixel-based features//Proceedings of 2014 IEEE International Joint Conference on Biometrics. Clearwater, USA: IEEE: 1-8[ DOI: 10.1109/BTAS.2014.6996232 http://dx.doi.org/10.1109/BTAS.2014.6996232 ]

Liu J W, Zha Z J, Chen D, Hong R C and Wang M. 2019b. Adaptive transfer network for cross-domain person re-identification//Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 7195-7204[ DOI: 10.1109/CVPR.2019.00737 http://dx.doi.org/10.1109/CVPR.2019.00737 ]

Liu J X, Ni B B, Yan Y C, Zhou P, Cheng S and Hu J G. 2018a. Pose transferrable person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 4099-4108[ DOI: 10.1109/CVPR.2018.00431 http://dx.doi.org/10.1109/CVPR.2018.00431 ]

Liu J Y and Zheng N N. 2007. Gait history image: a novel temporal template for gait recognition//Proceedings of 2017 IEEE International Conference on Multimedia and Expo. Beijing, China: IEEE: 663-666[ DOI: 10.1109/ICME.2007.4284737 http://dx.doi.org/10.1109/ICME.2007.4284737 ]

Liu L, Zhang D and You J. 2007a. Detecting wide lines using isotropic nonlinear filtering. IEEE Transactions on Image Processing, 16(6): 1584-1595[DOI:10.1109/TIP.2007.894288]

Liu M K, Wang H M, Li Q and Sun Z N. 2020. Enhanced gray-level image space for iris liveness detection. Journal of Image and Graphics, 25(7): 1421-1435

刘明康, 王宏民, 李琦, 孙哲南. 2020. 增强型灰度图像空间实现虹膜活体检测. 中国图象图形学报, 25(7): 1421-1435[DOI:10.11834/jig.190503]

Liu M Y and Tuzel O. 2016. Coupled generative adversarial networks//Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, United States: Curran Associates Inc. : 469-477

Liu N F, Li H Q, Zhang M, Liu J, Sun Z N and Tan T N. 2016a. Accurate iris segmentation in non-cooperative environments using fully convolutional networks//Proceedings of 2016 International Conference on Biometrics (ICB). Halmstad, Sweden: IEEE: 1-8[ DOI: 10.1109/ICB.2016.7550055 http://dx.doi.org/10.1109/ICB.2016.7550055 ]

Liu N F, Liu J, Sun Z N and Tan T N. 2017a. A code-level approach to heterogeneous iris recognition. IEEE Transactions on Information Forensics and Security, 12(10): 2373-2386[DOI:10.1109/TIFS.2017.2686013]

Liu W F, Pokharel P P and Principe J C. 2007b. Correntropy: properties and applications in non-Gaussian signal processing. IEEE Transactions on Signal Processing, 55(11): 5286-5298[DOI:10.1109/TSP.2007.896065]

Liu W Y, Wen Y D, Yu Z D and Yang M. 2016b. Large-margin softmax loss for convolutional neural networks[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1612.02295.pdf https://arxiv.org/pdf/1612.02295.pdf

Liu W Y, Wen Y D, Yu Z D, Li M, Raj B and Song L. 2017b. SphereFace: deep hypersphere embedding for face recognition//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 6738-6746[ DOI: 10.1109/CVPR.2017.713 http://dx.doi.org/10.1109/CVPR.2017.713 ]

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016c. SSD: single shot multibox detector//Proceedings European Conference on Computer Vision. Amsterdam, the Netherlands: Springer: 21-37[ DOI: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ]

Liu X W, Pedersen M, Charrier C and Bours P. 2017c. Can no-reference image quality metrics assess visible wavelength iris sample quality//Proceedings of 2017 IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE: 3530-3534[ DOI: 10.1109/ICIP.2017.8296939 http://dx.doi.org/10.1109/ICIP.2017.8296939 ]

Liu Y J, Jourabloo A and Liu X M. 2018b. Learning deep models for face anti-spoofing: binary or auxiliary supervision//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 389-398[ DOI: 10.1109/CVPR.2018.00048 http://dx.doi.org/10.1109/CVPR.2018.00048 ]

Liu Y J, Stehouwer J, Jourabloo A and Liu X M. 2019c. Deep tree learning for zero-shot face anti-spoofing//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 4675-4684[ DOI: 10.1109/CVPR.2019.00481 http://dx.doi.org/10.1109/CVPR.2019.00481 ]

Liu Z M, Wang D and Lu H C. 2017d. Stepwise metric promotion for unsupervised video person re-identification//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2448-2457[ DOI: 10.1109/ICCV.2017.266 http://dx.doi.org/10.1109/ICCV.2017.266 ]

Llano E G, Vargas J M C, García-Vázquez M S, Fuentes L M Z and Ramírez-Acosta A A. 2015. Cross-sensor iris verification applying robust fused segmentation algorithms//Proceedings of 2015 International Conference on Biometrics (ICB). Phuket, Thailand: IEEE: 17-22[ DOI: 10.1109/ICB.2015.7139042 http://dx.doi.org/10.1109/ICB.2015.7139042 ]

Llano E G, Vázquez M S G, Vargas J M C, Fuentes L M Z and Acosta A A R. 2018. Optimized robust multi-sensor scheme for simultaneous video and image iris recognition. Pattern Recognition Letters, 101: 44-51[DOI:10.1016/j.patrec.2017.11.012]

Loffe S. 2006. Probabilistic linear discriminant analysis//Proceedings of European Conference on Computer Vision. Graz, Austria: IEEE: 531-542[ DOI: 10.1007/11744085_41 http://dx.doi.org/10.1007/11744085_41 ]

López-Fernández D, Madrid-Cuevas F J, Carmona-Poyato A, Muñoz-Salinas R and Medina-Carnicer R. 2016. A new approach for multi-view gait recognition on unconstrained paths. Journal of Visual Communication and Image Representation, 38: 396-406[DOI:10.1016/j.jvcir.2016.03.020]

Lu G M, Zhang D and Wang K Q. 2003. Palmprint recognition using eigenpalms features. Pattern Recognition Letters, 24(9/10): 1463-1467[DOI:10.1016/S0167-8655(02)00386-0]

Lu H P, Plataniotis K N and Venetsanopoulos A N. 2007. A full-body layered deformable model for automatic model-based gait recognition. EURASIP Journal on Advances in Signal Processing, 2008: 261317[DOI:10.1155/2008/261317]

Lu J J, Sibai H and Fabry E. 2017. Adversarial examples that fool detectors[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1712.02494 https://arxiv.org/pdf/1712.02494

Lu J W, Wang G and Moulin P. 2014. Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 9(1): 51-61[DOI:10.1109/TIFS.2013.2291969]

Lu J, Zhao Y and Hu J. 2009a. Enhanced gabor-based region covariance matrices for palmprint recognition. Electronics Letters, 45(17): 880-881[DOI:10.1049/el.2009.0871]

Lu L, Dong Y, Zhao X Y, Liu J Q and Wang H L. 2009b. The effect of language factors for robust speaker recognition//Proceedings of 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing. Taipei, China: IEEE: 4217-4220[ DOI: 10.1109/ICASSP.2009.4960559 http://dx.doi.org/10.1109/ICASSP.2009.4960559 ]

Lu Y, Xie S J, YoonS, Wang Z H and Park D S. 2013. An available database for the research of finger vein recognition//Proceedings of the 6th International Congress on Image and Signal Processing (CISP). Hangzhou, China: IEEE: 410-415[ DOI: 10.1109/CISP.2013.6744030 http://dx.doi.org/10.1109/CISP.2013.6744030 ]

Lumini A and Nanni L. 2017. Overview of the combination of biometric matchers. Information Fusion, 33: 71-85[DOI:10.1016/j.inffus.2016.05.003]

Luo C C, Chen Y T, Wang N Y and Zhang Z X. 2019. Spectral feature transformation for person re-identification//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 4975-4984[ DOI: 10.1109/ICCV.2019.00508 http://dx.doi.org/10.1109/ICCV.2019.00508 ]

Lyu J M and Wang X T. 2018. Cross-dataset person re-identification using similarity preserved generative adversarial networks//Proceeding International Conference on Knowledge Science, Engineering and Management. Changchun, China: Springer: 171-183[ DOI: 10.1007/978-3-319-99247-1_15 http://dx.doi.org/10.1007/978-3-319-99247-1_15 ]

Ma B P, Su Y and Jurie F. 2012. BiCov: a novel image representation for person re-identification and face verification//Proceedings of the British Machine Vision Conference. Surrey: BMVA Press: #57[ DOI: 10.5244/C.26.57 http://dx.doi.org/10.5244/C.26.57 ]

Ma L, Tan T N, Wang Y H and Zhang D X. 2003. Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12): 1519-1533[DOI:10.1109/TPAMI.2003.1251145]

Ma X, Jing X J, Huang H, Cui Y H and Mu J S. 2017. Palm vein recognition scheme based on an adaptive Gabor filter. IET Biometrics, 6(5): 325-333[DOI:10.1049/iet-bmt.2016.0085]

Ma Z C, Fang L Y, Duan J H, Xie S and Wang Z Q. 2016. Personal identification based on finger vein and contour point clouds matching//Proceedings of 2016 IEEE International Conference on Mechatronics and Automation. Harbin, China: IEEE: 1983-1988[ DOI: 10.1109/ICMA.2016.7558870 http://dx.doi.org/10.1109/ICMA.2016.7558870 ]

MäättäJ, Hadid A and Pietikäinen M. 2011. Face spoofing detection from single images using micro-texture analysis//Proceedings of 2011 International Joint Conference on Biometrics (IJCB). Washington, USA: IEEE: 1-7[ DOI: 10.1109/IJCB.2011.6117510 http://dx.doi.org/10.1109/IJCB.2011.6117510 ]

MacGregor P and Welford R. 1991. Veincheck: imaging for security and personnel identification. Advanced Imaging, 6(7): 52-56

Makihara Y, Mannami H, Tsuji A, Hossain M A, Sugiura K, Mori A and Yagi Y. 2012. The OU-ISIR gait database comprising the treadmill dataset. IPSJ Transactions on Computer Vision and Applications, 4: 53-62[DOI:10.2197/ipsjtcva.4.53]

Makthal S and Ross A. 2005. Synthesis of iris images using Markov random fields//Proceedings of the 13th European Signal Processing Conference. Antalya, Turkey: IEEE: 1-4

Maltoni D, Maio D, Jain A K and Prabhakar S. 2009. Handbook of Fingerprint Recognition. 2nd ed. London: Springer

Mao X D, Li Q, Xie H R, Lau R Y K, Wang Z and Smolley S P. 2017. Least squares generative adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2813-2821[ DOI: 10.1109/ICCV.2017.304 http://dx.doi.org/10.1109/ICCV.2017.304 ]

Marra F, Gragnaniello D, Cozzolino D and Verdoliva L. 2018. Detection of GAN-generated fake images over social networks//Proceedings of 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). Miami, USA: IEEE: 384-389[ DOI: 10.1109/MIPR.2018.00084 http://dx.doi.org/10.1109/MIPR.2018.00084 ]

Marra F, Gragnaniello D, Verdoliva L and Poggi G. 2019. Do GANs leave artificial fingerprints//Proceedings of 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). San Jose, USA: IEEE: 506-511[ DOI: 10.1109/MIPR.2019.00103 http://dx.doi.org/10.1109/MIPR.2019.00103 ]

Marsico M D, Nappi M, Riccio D and Wechsler H. 2021. MICHE I-Ⅱ[EB/OL]. [2021-02-03] . http://biplab.unisa.it/MICHE/index_miche.html http://biplab.unisa.it/MICHE/index_miche.html

Matovski D S, Nixon M S, Mahmoodi S and Carter J N. 2012. The effect of time on gait recognition performance. IEEE Transactions on Information Forensics and Security, 7(2): 543-552[DOI:10.1109/TIFS.2011.2176118]

Maze B, Adams J, Duncan J A, Kalka N, Miller T, Otto C, Jain A K, Niggel W T, Anderson J, Cheney J and Grother P. 2018. IARPA Janus benchmark-C: face dataset and protocol//Proceedings of 2018 International Conference on Biometrics (ICB). Gold Coast, Australia: IEEE: 158-165[ DOI: 10.1109/ICB2018.2018.00033 http://dx.doi.org/10.1109/ICB2018.2018.00033 ]

McCloskey S and Albright M. 2018. Detecting GAN-generated imagery using color cues[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1812.08247.pdf https://arxiv.org/pdf/1812.08247.pdf

McGrath J, Bowyer K W and Czajka A. 2018. Open source presentation attack detection baseline for iris recognition[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1809.10172.pdf https://arxiv.org/pdf/1809.10172.pdf

McLaren M, Ferrer L, Castan D and Lawson A. 2016. The 2016 speakers in the wild speaker recognition evaluation//Proceedings of the INTERSPEECH 2016. San Francisco, USA: [s. n.]: 823-827[ DOI: 10.21437/Interspeech.2016-1137 http://dx.doi.org/10.21437/Interspeech.2016-1137 ]

Men Y F, Mao Y M, Jiang Y N, Ma W Y and Lian Z H. 2020. Controllable person image synthesis with attribute-decomposed GAN//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 5083-5092[ DOI: 10.1109/CVPR42600.2020.00513 http://dx.doi.org/10.1109/CVPR42600.2020.00513 ]

Menaria L and Jain K. 2017. A survey on biometric template protection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2(2): 995-999[DOI:10.1186/1687-417X-2011-3]

Meng J K, Wu S and Zheng W S. 2019. Weakly supervised person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 760-769[ DOI: 10.1109/CVPR.2019.00085 http://dx.doi.org/10.1109/CVPR.2019.00085 ]

Miao J X, Wu Y, Liu P, Ding Y H and Yang Y. 2019. Pose-guided feature alignment for occluded person re-identification//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 542-551[ DOI: 10.1109/ICCV.2019.00063 http://dx.doi.org/10.1109/ICCV.2019.00063 ]

Miau D, Cossairt O and Nayar S K. 2013. Focal sweep videography with deformable optics//Proceedings of 2013 IEEE International Conference on Computational Photography. Cambridge, USA: IEEE: 1-8[ DOI: 10.1109/iccphot.2013.6528302 http://dx.doi.org/10.1109/iccphot.2013.6528302 ]

Minaee S, Abdolrashidi A and Wang Y. 2017. An experimental study of deep convolutional features for iris recognition//Proceedings of 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). Philadelphia, USA: IEEE: 1-6[ DOI: 10.1109/SPMB.2016.7846859 http://dx.doi.org/10.1109/SPMB.2016.7846859 ]

Mirjalili V, Raschka S and Ross A. 2020. PrivacyNet: semi-adversarial networks for multi-attribute face privacy[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/2001.00561 https://arxiv.org/pdf/2001.00561

Mirjalili V and Ross A. 2017. Soft biometric privacy: retaining biometric utility of face images while perturbing gender//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA, IEEE, 2017: 564-573[ DOI: 10.1109/BTAS.2017.8272743. http://dx.doi.org/10.1109/BTAS.2017.8272743. ]

Mirmohamadsadeghi L and Drygajlo A. 2014. Palm vein recognition with local texture patterns. IET Biometrics, 3(4): 198-206[DOI:10.1049/iet-bmt.2013.0041]

Miura N, Nagasaka A and Miyatake T. 2004. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications, 15(4): 194-203[DOI:10.1007/s00138-004-0149-2]

Miura N, Nagasaka A and Miyatake T. 2005. Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE TRANSACTIONS on Information and Systems, E90-D(8): 1185-1194[DOI:10.1093/ietisy/e90-d.8.1185]

Mochizuki S, Shiota S and Kiya H. 2018. Voice liveness detection using phoneme-based pop-noise detector for speaker verification//Proceedings of 2018 Odyssey the Speaker and Language Recognition Workshop. Les Sables d'Olonne, France: [s. n.]: 233-239[ DOI: 10.21437/Odyssey.2018-33 http://dx.doi.org/10.21437/Odyssey.2018-33 ]

Monrose F, Reiter M K and Wetzel S. 2002. Password hardening based on keystroke dynamics. International Journal of Information Security, 1(2): 69-83[DOI:10.1007/s102070100006]

Monwar M M and Gavrilova M. 2008. FES: a system for combining face, ear and signature biometrics using rank level fusion//Proceedings of the 5th International Conference on Information Technology: New Generations. Las Vegas, USA: IEEE: 922-927[ DOI: 10.1109/ITNG.2008.254 http://dx.doi.org/10.1109/ITNG.2008.254 ]

Mu G D, Huang D, Hu G S, SunJ and Wang Y H. 2019. Led3D: a lightweight and efficient deep approach to recognizing low-quality 3D faces//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 5766-5775[ DOI: 10.1109/CVPR.2019.00592 http://dx.doi.org/10.1109/CVPR.2019.00592 ]

Murase H and Sakai R. 1996. Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters, 17(2): 155-162[DOI:10.1016/0167-8655(95)00109-3]

Nagrani A, Chung J S and Zisserman A. 2017. VoxCeleb: a large-scale speaker identification dataset[EB/OL][2021-02-03] . https://arxiv.org/pdf/1706.08612.pdf https://arxiv.org/pdf/1706.08612.pdf

Nalla P R and Kumar A. 2017. Toward more accurate iris recognition using cross-spectral matching. IEEE Transactions on Image Processing, 26(1): 208-221[DOI:10.1109/TIP.2016.2616281]

Nataraj L, Mohammed T M, Chandrasekaran S, Flenner A, Bappy J H, Roy-Chowdhury A K and Manjunath B S. 2019. Detecting GAN generated fake images using co-occurrence matrices[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1903.06836.pdf https://arxiv.org/pdf/1903.06836.pdf

Natsume R, Yatagawa T and Morishima S. 2018a. FSNet: an identity-aware generative model for image-based face swapping//Proceedings of Asian Conference on Computer Vision. Perth, Australia: Springer: 117-132[ DOI: 10.1007/978-3-030-20876-9_8 http://dx.doi.org/10.1007/978-3-030-20876-9_8 ]

Natsume R, Yatagawa T and Morishima S. 2018b. RSGAN: face swapping and editing using face and hair representation in latent spaces[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1804.03447.pdf https://arxiv.org/pdf/1804.03447.pdf

Nguyen D L, Arora S S, Wu Y H and Yang H. 2020. Adversarial light projection attacks on face recognition systems: a feasibility study[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/2003.11145.pdf https://arxiv.org/pdf/2003.11145.pdf

Nguyen D L, Cao K and Jain A K. 2018. Robust minutiae extractor: integrating deep networks and fingerprint domain knowledge//Proceedings of 2018 International Conference on Biometrics (ICB). Gold Coast, Australia: IEEE: 9-16[ DOI: 10.1109/ICB2018.2018.00013 http://dx.doi.org/10.1109/ICB2018.2018.00013 ]

Nguyen H H, Yamagishi J and Echizen I. 2019. Capsule-forensics: using capsule networks to detect forged images and videos//Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing. Brighton, UK: IEEE: 2307-2311[ DOI: 10.1109/ICASSP.2019.8682602 http://dx.doi.org/10.1109/ICASSP.2019.8682602 ]

Nichols R K. 1998. ICSA Guide to Cryptography. New York: McGraw-Hill

Nirkin Y, Keller Y and Hassner T. 2019. FSGAN: subject agnostic face swapping and reenactment//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 7183-7192[ DOI: 10.1109/ICCV.2019.00728 http://dx.doi.org/10.1109/ICCV.2019.00728 ]

Niu X S, Shan S G, Han H and Chen X L. 2020a. RhythmNet: end-to-end heart rate estimation from face via spatial-temporal representation. IEEE Transactions on Image Processing, 29: 2409-2423[DOI:10.1109/TIP.2019.2947204]

Niu X S, Yu Z T, Han H, Li X B, Shan S G and Zhao G Y. 2020b. Video-based remote physiological measurement via cross-verified feature disentangling//Proceedings of European Conference on Computer Vision. Glasgow, UK: Springer: 295-310[ DOI: 10.1007/978-3-030-58536-5_18 http://dx.doi.org/10.1007/978-3-030-58536-5_18 ]

Nixon M, Carter J, Shufler J and Grant M. 2001. Experimental Plan for Automatic Gait Recognition. University of Southampton, Southampton

OrrùG, Casula R, Tuveri P, Bazzoni C, Dessalvi G, Micheletto M, Ghiani L and Marcialis G L. 2019. LivDet in action-fingerprint liveness detection competition 2019//Proceedings of 2019 International Conference on Biometrics (ICB). Crete, Greece: IEEE: 1-6[ DOI: 10.1109/ICB45273.2019.8987281 http://dx.doi.org/10.1109/ICB45273.2019.8987281 ]

Pala P, Seidenari L, Berretti S and Del Bimbo A. 2019. Enhanced skeleton and face 3D data for person re-identification from depth cameras. Computers and Graphics, 79: 69-80[DOI:10.1016/j.cag.2019.01.003]

Pan G, Sun L, Wu Z H and Lao S H. 2007. Eyeblink-based anti-spoofing in face recognition from a generic webcamera//Proceedings of the 11th International Conference on Computer Vision. Rio de Janeiro, Brazil: IEEE: 1-8[ DOI: 10.1109/ICCV.2007.4409068 http://dx.doi.org/10.1109/ICCV.2007.4409068 ]

Pan M and Kang W X. 2011. Palm vein recognition based on three local invariant feature extraction algorithms//Proceedings Chinese Conference on Biometric Recognition. Beijing, China: IEEE: 116-124[ DOI: 10.1007/978-3-642-25449-9_15 http://dx.doi.org/10.1007/978-3-642-25449-9_15 ]

Park D S, Chan W, Zhang Y, Chiu C C, Zoph B, Cubuk E D and Le Q V. 2019a. SpecAugment: a simple data augmentation method for automatic speech recognition[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1904.08779 https://arxiv.org/pdf/1904.08779

Park T, Liu M Y, Wang T C and Zhu J Y. 2019b. Semantic image synthesis with spatially-adaptive normalization//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 2332-2341[ DOI: 10.1109/CVPR.2019.00244 http://dx.doi.org/10.1109/CVPR.2019.00244 ]

Parziale G, Diaz-Santana E and Hauke R. 2006. The surround imager TM : a multi-camera touchless device to acquire 3D rolled-equivalent fingerprints//Proceedings of International Conference on Biometrics. Hong Kong, China: Springer: 244-250[ DOI: 10.1007/11608288_33 http://dx.doi.org/10.1007/11608288_33 ].

Patil H A and Kamble M R. 2018. A survey on replay attack detection for automatic speaker verification (ASV)system//Proceedings of 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). Honolulu, USA: IEEE: 1047-1053[ DOI: 10.23919/APSIPA.2018.8659666 http://dx.doi.org/10.23919/APSIPA.2018.8659666 ]

Patil I, Bhilare S and Kanhangad V. 2016. Assessing vulnerability of dorsal hand-vein verification system to spoofing attacks using smartphone camera//Proceedings of 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). Sendai, Japan: IEEE: 1-6[ DOI: 10.1109/ISBA.2016.7477232 http://dx.doi.org/10.1109/ISBA.2016.7477232 ]

Peng Y G, Ganesh A, Wright J, Xu W L and Ma Y. 2012. RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2233-2246[DOI:10.1109/TPAMI.2011.282]

Perwira D Y, Agung B W T and Sulistiyo M D. 2014. Personal palm vein identification using principal component analysis and probabilistic neural network//Proceedings of 2014 International Conference on Information Technology Systems and Innovation (ICITSI). Bandung, Indonesia: IEEE: 99-104[ DOI: 10.1109/ICITSI.2014.7048245 http://dx.doi.org/10.1109/ICITSI.2014.7048245 ]

Phillips P J, Flynn P J, Scruggs T, Bowyer K W, Chang J, Hoffman K, Marques J, Min J and Worek W. 2005. Overview of the face recognition grand challenge//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE: 947-954[ DOI: 10.1109/CVPR.2005.268 http://dx.doi.org/10.1109/CVPR.2005.268 ]

Piciucco E, Maiorana E and Campisi P. 2018. Palm vein recognition using a high dynamic range approach. IET Biometrics, 7(5): 439-446[DOI:10.1049/iet-bmt.2017.0192]

Pillai J K, Patel V M, Chellappa R and Ratha N K. 2011. Secure and robust iris recognition using random projections and sparse representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9): 1877-1893[DOI:10.1109/TPAMI.2011.34]

Pillai J K, Puertas M and Chellappa R. 2014. Cross-sensor iris recognition through kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1): 73-85[DOI:10.1109/TPAMI.2013.98]

PLUSVein-FV3 Finger Vein Data Set[EB/OL]. [2021-02-03] . http://www.wavelab.at/sources/PLUSVein-FV3/ http://www.wavelab.at/sources/PLUSVein-FV3/

Poh M Z, McDuff D J and Picard R W. 2010. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express, 18(10): 10762-10774[DOI:10.1364/OE.18.010762]

Proença H, Filipe S, Santos R, Oliveira J and Alexandre L A. 2021. UBIRIS V1-V2[EB/OL]. [2021-02-03] . http://iris.di.ubi.pt/ubiris2.html http://iris.di.ubi.pt/ubiris2.html

Proença H. 2010. Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8): 1502-1516[DOI:10.1109/TPAMI.2009.140]

Prommegger B, Kauba C, Linortner M and Uhl A. 2019. Longitudinal finger rotation-deformation detection and correction. IEEE Transactions on Biometrics, Behavior, and Identity Science, 1(2): 123-138[DOI:10.1109/TBIOM.2019.2902020]

Pumarola A, Agudo A, Martinez A M, Sanfeliu A and Moreno-Noguer F. 2018. GANimation: anatomically-aware facial animation from a single image//Proceedings of European Conference on Computer Vision. Munich, Germany: Springer: 835-851[ DOI: 10.1007/978-3-030-01249-6_50 http://dx.doi.org/10.1007/978-3-030-01249-6_50 ]

Qian X L, Fu Y W, Jiang Y G, Tao X and Xue X Y. 2017. Multi-scale deep learning architectures for person re-identification//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 5409-5418[ DOI: 10.1109/ICCV.2017.577 http://dx.doi.org/10.1109/ICCV.2017.577 ]

Qian X L, Fu Y W, Xiang T, Wang W X, Qiu J, Wu Y, Jiang Y G and Xue X Y. 2018. Pose-normalized image generation for person re-identification[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1712.02225v6.pdf https://arxiv.org/pdf/1712.02225v6.pdf

Qian X L, Wang W X, Zhang L, Zhu F R, Fu Y W, Xiang T, Jiang Y G and Xue X Y. 2020. Long-term cloth-changing person re-identification[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/2005.12633.pdf https://arxiv.org/pdf/2005.12633.pdf

Qin H W, Yan J J, Li X and Hu X L. 2016. Joint training of cascaded CNN for face detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 3456-3465[ DOI: 10.1109/CVPR.2016.376 http://dx.doi.org/10.1109/CVPR.2016.376 ]

Qin Y X, Zhao C X, Zhu X Y, Wang Z Z, Yu Z T, Fu T Y, Zhou F, Shi J P and Lei Z. 2020. Learning meta model for zero- and few-shot face anti-spoofing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(7): 11916-11923[DOI:10.1609/aaai.v34i07.6866]

Qiu X W, Kang W X, Tian S P, Jia W and Huang Z X. 2018. Finger vein presentation attack detection using total variation decomposition. IEEE Transactions on Information Forensics and Security, 13(2): 465-477[DOI:10.1109/TIFS.2017.2756598]

Radford A, Metz L K and Chintala S. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1511.06434.pdf https://arxiv.org/pdf/1511.06434.pdf

Ranjan R, Castillo C D and Chellappa R. 2017. L 2 -constrained softmax loss for discriminative face verification[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1703.09507.pdf https://arxiv.org/pdf/1703.09507.pdf .

Ratha N K, Chikkerur S, Connell J H and Bolle R M. 2007. Generating cancelable fingerprint templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4): 561-572[DOI:10.1109/TPAMI.2007.1004]

Rathgeb C and Uhl A. 2011. A survey on biometric cryptosystems and cancelable biometrics. EURASIP Journal on Information Security, 2011: #3[DOI:10.1186/1687-417X-2011-3]

Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 779-788[ DOI: 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ]

Ren M, Wang C Y, Wang Y L, Sun Z N and Tan T N. 2019. Alignment free and distortion robust iris recognition//Proceedings of 2019 International Conference on Biometrics (ICB). Crete, Greece: IEEE: 1-7[ DOI: 10.1109/ICB45273.2019.8987369 http://dx.doi.org/10.1109/ICB45273.2019.8987369 ]

Ren M, Wang Y L, Sun Z N and Tan T N. 2020. Dynamic graph representation for occlusion handling in biometrics//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI: 11940-11947

Ren S Q, Cao X D, Wei Y C and Sun J. 2014. Face alignment at 3000 FPS via regressing local binary features//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 1685-1692[ DOI: 10.1109/CVPR.2014.218 http://dx.doi.org/10.1109/CVPR.2014.218 ]

Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149[DOI:10.1109/TPAMI.2016.2577031]

Reynolds D A, Quatieri T F and Dunn R B. 2000. Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 10(1/3): 19-41[DOI:10.1006/dspr.1999.0361]

Reynolds D A. 2009. Gaussian mixture models//Proceedings of Encyclopedia of Biometrics. Boston, MA, USA: Springer: 827-832[ DOI: 10.1007/978-1-4899-7488-4_196 http://dx.doi.org/10.1007/978-1-4899-7488-4_196 ]

Rice J. 1987. Apparatus for the identification of individuals. U.S., No. 4699149

Rigas I and Komogortsev O V. 2015. Eye movement-driven defense against iris print-attacks. Pattern Recognition Letters, 68: 316-326[DOI:10.1016/j.patrec.2015.06.011]

Ronneberger O, Fischer P and Brox T. 2015. U-net: convolutional networks for biomedical image segmentation//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens, Greece: Springer: 234-241[ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Ross A S and Doshi-Velez F. 2017. Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1711.09404.pdf https://arxiv.org/pdf/1711.09404.pdf

Rozendal S. 2018. Redesign of a Finger Vein Scanner. The Netherlands: University of Twente

Rozsa A, Günther M and Boult T E. 2017. LOTS about attacking deep features//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver, USA: IEEE: 168-176[ DOI: 10.1109/BTAS.2017.8272695 http://dx.doi.org/10.1109/BTAS.2017.8272695 ]

Rozsa A, Günther M, Rudd E M and Boult T E. 2016. Are facial attributes adversarially robust//Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE: 3121-3127[ DOI: 10.1109/ICPR.2016.7900114 http://dx.doi.org/10.1109/ICPR.2016.7900114 ]

Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J and Nieβner M. 2018. FaceForensics: a large-scale video dataset for forgery detection in human faces[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1803.09179.pdf https://arxiv.org/pdf/1803.09179.pdf

Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J and Nieβner M. 2019. FaceForensics++: learning to detect manipulated facial images[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1901.08971.pdf https://arxiv.org/pdf/1901.08971.pdf

Sabir E, Cheng J X, Jaiswal A, AbdAlmageed W, Masi I and Natarajan P. 2019. Recurrent convolutional strategies for face manipulation detection in videos[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1905.00582.pdf https://arxiv.org/pdf/1905.00582.pdf

Sabour S, Frosst N and Hinton G E. 2017. Dynamic routing between capsules[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1710.09829.pdf https://arxiv.org/pdf/1710.09829.pdf

Sadjadi S O, Greenberg C, Singer E, Reynolds D, Mason L and Hernandez-Cordero J. 2020. The 2019 NIST Audio-visual Speaker Recognition Evaluation. Speaker Odyssey

Sadjadi S O, Kheyrkhah T, Tong A, Greenberg C, Reynolds D, Singer E, Mason L and Hernandez-Cordero J. 2017. The 2016 NIST speaker recognition evaluation//Proceedings of the INTERSPEECH 2017. Stockholm, Sweden: [s. n.]: 1353-1357

Sagonas C, Antonakos E, Tzimiropoulos G, Zafeiriou S and Pantic M. 2016. 300 faces in-the-wild challenge: database and results. Image and Vision Computing, 47: 3-18[DOI:10.1016/j.imavis.2016.01.002]

Satoh T, Masuko T, Kobayashi T and Tokuda K. 2001. A robust speaker verification system against imposture using an HMM-based speech synthesis system//Proceedings of the 7th European Conference on Speech Communication and Technology. Aalborg, Denmark: ISCA: 759-762

Schroff F, Kalenichenko D and Philbin J. 2015. FaceNet: a unified embedding for face recognition and clustering//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 815-823[ DOI: 10.1109/CVPR.2015.7298682 http://dx.doi.org/10.1109/CVPR.2015.7298682 ]

Schuch P, Schulz S D and Busch C. 2017. Deep expectation for estimation of fingerprint orientation fields//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 185-190[ DOI: 10.1109/BTAS.2017.8272697 http://dx.doi.org/10.1109/BTAS.2017.8272697 ]

Seely R D, Samangooei S, Lee M, Carter J N and Nixon M S. 2008. The university of southampton multi-biometric tunnel and introducing a novel 3D gait dataset//Proceedings of the 2nd International Conference on Biometrics: Theory, Applications, and Systems. Washington, USA: IEEE: 1-6[ DOI: 10.1109/BTAS.2008.4699353 http://dx.doi.org/10.1109/BTAS.2008.4699353 ]

Shah S and Ross A. 2009. Iris segmentation using geodesic active contours. IEEE Transactions on Information Forensics and Security, 4(4): 824-836[DOI:10.1109/TIFS.2009.2033225]

Shakhnarovich G, Lee L and Darrell T. 2001. Integrated face and gait recognition from multiple views//Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA: IEEE: 439-446[ DOI: 10.1109/CVPR.2001.990508 http://dx.doi.org/10.1109/CVPR.2001.990508 ]

Shang W and Stevenson M. 2008. A preliminary study of factors affecting the performance of a playback attack detector//Proceedings of 2008 Canadian Conference on Electrical and Computer Engineering. Niagara Falls, Canada: IEEE: 459-464[ DOI: 10.1109/CCECE.2008.4564576 http://dx.doi.org/10.1109/CCECE.2008.4564576 ]

Sharif M, Bhagavatula S, Bauer L and Reiter M K. 2016. Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition//Proceedings of 2016 ACM SIGSAC Conference on Computer and Communications Security. Vienna, Austria: ACM: 1528-1540[ DOI: 10.1145/2976749.2978392 http://dx.doi.org/10.1145/2976749.2978392 ]

Sharif M, Bhagavatula S, Bauer L and Reiter M K. 2017. Adversarial generative nets: neural network attacks on state-of-the-art face recognition[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1801.00349v1.pdf https://arxiv.org/pdf/1801.00349v1.pdf

Sharma R and Ross A. 2020. D-NetPAD: an explainable and interpretable iris presentation attack detector//Proceedings of 2020 IEEE International Joint Conference on Biometrics (IJCB). Houston, USA: IEEE: 1-10[ DOI: 10.1109/IJCB48548.2020.9304880 http://dx.doi.org/10.1109/IJCB48548.2020.9304880 ]

Shen M, Liao Z L, Zhu L H, Xu K and Du X J. 2019. VLA: a practical visible light-based attack on face recognition systems in physical world. ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, 3(3): #103[DOI:10.1145/3351261]

Shen Y J, Luo P, Luo P, Yan J J, Wang X G and Tang X O. 2018a. FaceID-GAN: learning a symmetry three-player GAN for identity-preserving face synthesis//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 821-830[ DOI: 10.1109/CVPR.2018.00092 http://dx.doi.org/10.1109/CVPR.2018.00092 ]

Shen Y T, Xiao T, Li H S, Yi S and Wang X G. 2018b. End-to-end deep kronecker-product matching for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 6886-6895[ DOI: 10.1109/CVPR.2018.00720 http://dx.doi.org/10.1109/CVPR.2018.00720 ]

Shi Q F, Eriksson A, van den Hengel A and Shen C H. 2011. Is face recognition really a compressive sensing problem//Proceedings of the CVPR 2011. Colorado Springs, USA: IEEE: 553-560[ DOI: 10.1109/CVPR.2011.5995556 http://dx.doi.org/10.1109/CVPR.2011.5995556 ]

Shu W and Zhang D. 1998. Automated personal identification by palmprint. Optical Engineering, 37(8): 2359-2362[DOI:10.1117/1.601756]

Shutler J D, Nixon M S and Harris C J. 2000. Statistical gait description via temporal moments//Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation. Austin, USA: IEEE: 291-295[ DOI: 10.1109/IAI.2000.839618 http://dx.doi.org/10.1109/IAI.2000.839618 ]

Si X B, Feng J J, Yuan B and Zhou J. 2017. Dense registration of fingerprints. Pattern Recognition, 63: 87-101[DOI:10.1016/j.patcog.2016.09.012]

Si X B, Feng J J, Zhou J and Luo Y X. 2015. Detection and rectification of distorted fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3): 555-568[DOI:10.1109/TPAMI.2014.2345403]

Simonyan K and Zisserman A. 2014a. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2021-02-03] . https://export.arxiv.org/pdf/1409.1556.pdf https://export.arxiv.org/pdf/1409.1556.pdf

Simonyan K and Zisserman A. 2014b. Two-stream convolutional networks for action recognition in videos[EB/OL]. [2021-02-02] . https://arxiv.org/pdf/1406.2199.pdf https://arxiv.org/pdf/1406.2199.pdf

Snyder D, Garcia-Romero D, Sell G, Povey D and Khudanpur S. 2018. X-vectors: robust DNN embeddings for speaker recognition//Proceedings of 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Calgary, Canada: IEEE: 5329-5333[ DOI: 10.1109/ICASSP.2018.8461375 http://dx.doi.org/10.1109/ICASSP.2018.8461375 ]

Song P, Huang L, Wang Y L, Liu F and Sun Z N. 2019. Iris liveness detection based on light field imaging. Acta Automatica Sinica, 45(9): 1701-1712

宋平, 黄玲, 王云龙, 刘菲, 孙哲南. 2019. 基于计算光场成像的虹膜活体检测方法. 自动化学报, 45(9): 1701-1712[DOI:10.16383/j.aas.c180213]

Song Q, Wu Y Q and Yang L. 2018. Attacks on state-of-the-art face recognition using attentional adversarial attack generative network[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1811.12026.pdf https://arxiv.org/pdf/1811.12026.pdf

Sun Q L, Zhang J X, Yang A Q and Zhang Q. 2017. Palmprint recognition with deep convolutional features//Proceedings of Chinese Conference on Image and Graphics Technologies. Beijing, China: Springer: 12-19[ DOI: 10.1007/978-981-10-7389-2_2 http://dx.doi.org/10.1007/978-981-10-7389-2_2 ]

Sun X X and Zheng L. 2019. Dissecting person re-identification from the viewpoint of viewpoint//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 608-617[ DOI: 10.1109/CVPR.2019.00070 http://dx.doi.org/10.1109/CVPR.2019.00070 ]

Sun Y F, Xu Q, Li Y L, Zhang C, Li Y K, Wang S J and Sun J. 2019. Perceive where to focus: learning visibility-aware part-level features for partial person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 393-402[ DOI: 10.1109/CVPR.2019.00048 http://dx.doi.org/10.1109/CVPR.2019.00048 ]

Sun Y F, Zheng L, Yang Y, Tian Q and Wang S J. 2018. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)//Proceedings of European Conference on Computer Vision. Munich, Germany: Springer: 501-518[ DOI: 10.1007/978-3-030-01225-0_30 http://dx.doi.org/10.1007/978-3-030-01225-0_30 ]

Sun Y, Chen Y H, Wang X G and Tang X O. 2014a. Deep learning face representation by joint identification-verification//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press: 1988-1996[ DOI: 10.5555/2969033.2969049 http://dx.doi.org/10.5555/2969033.2969049 ]

Sun Y, Wang X G and Tang X O. 2013. Deep convolutional network cascade for facial point detection//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: 3476-3483[ DOI: 10.1109/CVPR.2013.446 http://dx.doi.org/10.1109/CVPR.2013.446 ]

Sun Y, Wang X G and Tang X O. 2014b. Deep learning face representation from predicting 10 000 classes//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 1891-1898[ DOI: 10.1109/CVPR.2014.244 http://dx.doi.org/10.1109/CVPR.2014.244 ]

Sun Z N and Tan T N. 2009. Ordinal measures for iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12): 2211-2226[DOI:10.1109/TPAMI.2008.240]

Sun Z N, Tan T N, Wang Y H and Li S Z. 2005. Ordinal palmprint represention for personal identification[represention read representation]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE: 279-284[ DOI: 10.1109/CVPR.2005.267 http://dx.doi.org/10.1109/CVPR.2005.267 ]

Sun Z N, Wang L B and Tan T N. 2014c. Ordinal feature selection for iris and palmprint recognition. IEEE Transactions on Image Processing, 23(9): 3922-3934[DOI:10.1109/TIP.2014.2332396]

Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 1-9[ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]

Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I and Fergus R. 2013. Intriguing properties of neural networks[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1312.6199.pdf https://arxiv.org/pdf/1312.6199.pdf

Taigman Y, Yang M, Ranzato M A and Wolf L. 2014. DeepFace: closing the gap to human-level performance in face verification//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 1701-1708[ DOI: 10.1109/CVPR.2014.220 http://dx.doi.org/10.1109/CVPR.2014.220 ]

Tak H and Patil H A. 2018. Novel linear frequency residual cepstral features for replay attack detection//Proceedings of the INTERSPEECH 2018. Hyderabad, India: [s. n.]: 726-730[ DOI: 10.21437/Interspeech.2018-1702 http://dx.doi.org/10.21437/Interspeech.2018-1702 ]

Takemura N, Makihara Y, Muramatsu D, Echigo T and Yagi Y. 2018. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Transactions on Computer Vision and Applications, 10: #4[DOI:10.1186/s41074-018-0039-6]

Tan T N and Sun Z N. 2021. CASIA Iris V1-V4[EB/OL]. [2021-02-03] . http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Iris http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Iris

Tang H L, Yin B C, Sun Y F and Hu Y L. 2013. 3D face recognition using local binary patterns. Signal Processing, 93(8): 2190-2198[DOI:10.1016/j.sigpro.2012.04.002]

Tang H, Bai S, Zhang L, Torr P H S and Sebe N. 2020. XingGAN for person image generation//Proceedings of European Conference on Computer Vision. Glasgow, United Kingdom: Springer: 717-734[ DOI: 10.1007/978-3-030-58595-2_43 http://dx.doi.org/10.1007/978-3-030-58595-2_43 ]

Tang J, Luo J, Tjahjadi T and Gao Y. 2014. 2.5D multi-view gait recognition based on point cloud registration. Sensors, 14(4): 6124-6143[DOI:10.3390/s140406124]

Tang J, Luo J, Tjahjadi T and Guo F. 2017a. Robust arbitrary-view gait recognition based on 3D partial similarity matching. IEEE Transactions on Image Processing, 26(1): 7-22[DOI:10.1109/TIP.2016.2612823]

Tang S, Zhou S, Kang W X, Wu Q X and Deng F Q. 2019. Finger vein verification using a Siamese CNN. IET Biometrics, 8(5): 306-315[DOI:10.1049/iet-bmt.2018.5245]

Tang Y, Gao F, Feng J F and Liu Y H. 2017b. FingerNet: an unified deep network for fingerprint minutiae extraction//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 108-116[ DOI: 10.1109/BTAS.2017.8272688 http://dx.doi.org/10.1109/BTAS.2017.8272688 ]

Tao G H, Ma S Q, Liu Y Q and Zhang X Y. 2018. Attacks meet interpretability: attribute-steered detection of adversarial samples[EB/OL]. [2021-01-21] . https://arxiv.org/pdf/1810.11580.pdf https://arxiv.org/pdf/1810.11580.pdf

Thapar D, Jaswal G, Nigam A and Kanhangad V. 2019. PVSNet: palm vein authentication Siamese network trained using triplet loss and adaptive hard mining by learning enforced domain specific features//Proceedings of the 5th IEEE International Conference on Identity, Security, and Behavior Analysis (ISBA). Hyderabad, India: IEEE: 1-8[ DOI: 10.1109/ISBA.2019.8778623 http://dx.doi.org/10.1109/ISBA.2019.8778623 ]

Theagarajan R and Bhanu B. 2020. Defending black box facial recognition classifiers against adversarial attacks//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, USA: IEEE: 3537-3547[ DOI: 10.1109/CVPRW50498.2020.00414 http://dx.doi.org/10.1109/CVPRW50498.2020.00414 ]

Thornton J, Savvides M and Kumar B V K V. 2007. A Bayesian approach to deformed pattern matching of iris images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4): 596-606[DOI:10.1109/TPAMI.2007.1006]

Tian M Q, Yi S, Li H S, Li S H, Zhang X S, Shi J P, Yan J J and Wang X G. 2018. Eliminating background-bias for robust person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5794-5803[ DOI: 10.1109/CVPR.2018.00607 http://dx.doi.org/10.1109/CVPR.2018.00607 ]

Tiwari K, Arya D K, Badrinath G S and Gupta P. 2013. Designing palmprint based recognition system using local structure tensor and force field transformation for human identification. Neurocomputing, 116: 222-230[DOI:10.1016/j.neucom.2011.12.065]

Todisco M, Delgado H and Evans N. 2017. Constant Q cepstral coefficients: a spoofing countermeasure for automatic speaker verification. Computer Speech and Language, 45: 516-535[DOI:10.1016/j.csl.2017.01.001]

Toh K A, Eng H L, Choo Y S, Cha Y L, Yau W Y and Low K S. 2006. Identity verification through palm vein and crease texture//Proceedings of International Conference on Biometrics. Hong Kong, China: Springer: 546-553[ DOI: 10.1007/11608288_73 http://dx.doi.org/10.1007/11608288_73 ]

Tome P and Marcel S. 2015. On the vulnerability of palm vein recognition to spoofing attacks//Proceedings of 2015 International Conference on Biometrics. Phuket, Thailand: IEEE: 319-325[ DOI: 10.1109/ICB.2015.7139056 http://dx.doi.org/10.1109/ICB.2015.7139056 ]

Tome P, Vanoni M and Marcel S. 2014. On the vulnerability of finger vein recognition to spoofing//Proceedings of 2014 International Conference of the Biometrics Special Interest Group (BIOSIG). Darmstadt, Germany: IEEE: 1-10

Ton B T and Veldhuis R N J. 2013. A high quality finger vascular pattern dataset collected using a custom designed capturing device//Proceedings of 2013 International Conference on Biometrics (ICB). Madrid, Spain: IEEE: 1-5[ DOI: 10.1109/ICB.2013.6612966 http://dx.doi.org/10.1109/ICB.2013.6612966 ]

Toygar O, Babalola F O and Bitirim Y. 2020. FYO: a novel multimodal vein database with palmar, dorsal and wrist biometrics. IEEE Access, 8: 82461-82470[DOI:10.1109/ACCESS.2020.2991475]

Trabelsi R B, Masmoudi A D and Masmoudi D S. 2015. A bi-modal palmvein palmprint biometric human identification based on fusing new CDSDP features//Proceedings of 2015 International Conference on Advances in Biomedical Engineering (ICABME). Beirut, Lebanon: IEEE: 1-4[ DOI: 10.1109/ICABME.2015.7323236 http://dx.doi.org/10.1109/ICABME.2015.7323236 ]

Turk M and Pentland A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1): 71-86[DOI:10.1162/jocn.1991.3.1.71]

Ungureanu A S, Salahuddin S and Corcoran P. 2020. Toward unconstrained palmprint recognition on consumer devices: a literature. IEEE Access, 8: 86130-86148[DOI:10.1109/ACCESS.2020.2992219]

Ungureanu A S, Thavalengal S, Cognard T E, Costache C and Corcoran P. 2017. Unconstrained palmprint as a smartphone biometric. IEEE Transactions on Consumer Electronics, 63(3): 334-342[DOI:10.1109/TCE.2017.014994]

Vanoni M, Tome P, El Shafey L and Marcel S. 2014. Cross-database evaluation using an open finger vein sensor//Proceedings of 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS). Rome, Italy: IEEE: 30-35[ DOI: 10.1109/BIOMS.2014.6951532 http://dx.doi.org/10.1109/BIOMS.2014.6951532 ]

Variani E, Lei X, McDermott E, Moreno I L and Gonzalez-Dominguez J. 2014. Deep neural networks for small footprint text-dependent speaker verification//Proceedings of 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Florence, Italy: IEEE: 4052-4056[ DOI: 10.1109/ICASSP.2014.6854363 http://dx.doi.org/10.1109/ICASSP.2014.6854363 ]

Vatsa M and Singh R. 2021. ⅢTD[EB/OL]. [2021-02-03] . http://iab-rubric.org/resources.html http://iab-rubric.org/resources.html

Venugopalan S and Savvides M. 2011. How to generate spoofed irises from an iris code template. IEEE Transactions on Information Forensics and Security, 6(2): 385-395[DOI:10.1109/TIFS.2011.2108288]

Venugopalan S, Prasad U, Harun K, Neblett K, Toomey D, Heyman J and Savvides M. 2011. Long range iris acquisition system for stationary and mobile subjects//Proceedings of 2011 International Joint Conference on Biometrics (IJCB). Washington, USA: IEEE: 1-8[ DOI: 10.1109/IJCB.2011.6117484 http://dx.doi.org/10.1109/IJCB.2011.6117484 ]

Verkruysse W, Svaasand L O and Nelson J S. 2008. Remote plethysmographic imaging using ambient light. Optics Express, 16(26): 21434-21445[DOI:10.1364/OE.16.021434]

Viola P and Jones M J. 2004. Robust real-time face detection. International Journal of Computer Vision, 57(2): 137-154[DOI:10.1023/B:VISI.0000013087.49260.fb]

Walia G S, Singh T, Singh K and Verma N. 2019. Robust multimodal biometric system based on optimal score level fusion model. Expert Systems with Applications, 116: 364-376[DOI:10.1016/j.eswa.2018.08.036]

Wang C Y and Sun Z N. 2020. A benchmark for iris segmentation. Journal of Computer Research and Development, 57(2): 395-412

王财勇, 孙哲南. 2020. 虹膜分割算法评价基准. 计算机研究与发展, 57(2): 395-412[DOI:10.7544/issn1000-1239.2020.20190092]

Wang C Y, Wang Y L, Xu B Q, He Y, Dong Z W and Sun Z N. 2020b. A lightweight multi-label segmentation network for mobile iris biometrics. A lightweight multi-label segmentation network for mobile iris biometrics//Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE: 1006-1010[ DOI: 10.1109/ICASSP40776.2020.9054353 http://dx.doi.org/10.1109/ICASSP40776.2020.9054353 ]

Wang C, Zhang J P, Wang L, Pu J and Yuan X R. 2012. Human identification using temporal information preserving gait template. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2164-2176[DOI:10.1109/TPAMI.2011.260]

Wang C, Zhang Q, Huang C, Liu W Y and Wang X G. 2018a. Mancs: a multi-task attentional network with curriculum sampling for person re-identification//European Conference on Computer Vision. Munich, Germany: Springer: 384-400[ DOI: 10.1007/978-3-030-01225-0_23 http://dx.doi.org/10.1007/978-3-030-01225-0_23 ]

Wang F, Xiang X, Cheng J and Yuille A L. 2017a. NormFace: L 2 hypersphere embedding for face verification//Proceedings of the 25th ACM International Conference on Multimedia. Mountain View, USA: ACM: 1041-1049[ DOI: 10.1145/3123266.3123359 http://dx.doi.org/10.1145/3123266.3123359 ].

Wang G A, Yang S, Liu H Y, Wang Z C, Yang Y, Wang S L, Yu G, Zhou E J and Sun J. 2020c. High-order information matters: learning relation and topology for occluded person re-identification//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 6448-6457[ DOI: 10.1109/CVPR42600.2020.00648 http://dx.doi.org/10.1109/CVPR42600.2020.00648 ]

Wang G A, Zhang T Z, Cheng J, Liu S, Yang Y and Hou Z G. 2019a. RGB-infrared cross-modality person re-identification via joint pixel and feature alignment//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 3622-3631[ DOI: 10.1109/ICCV.2019.00372 http://dx.doi.org/10.1109/ICCV.2019.00372 ]

Wang G Q, Han H, Shan S G and Chen X L. 2020d. Cross-domain face presentation attack detection via multi-domain disentangled representation learning//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 6677-6686[ DOI: 10.1109/CVPR42600.2020.00671 http://dx.doi.org/10.1109/CVPR42600.2020.00671 ]

Wang G Q, Sun C M and Sowmya A. 2020e. Learning a compact vein discrimination model with GANerated samples. IEEE Transactions on Information Forensics and Security, 15: 635-650[DOI:10.1109/TIFS.2019.2924553]

Wang G R, Wang G C, Zhang X J, Lai J H and Lin L. 2019b. Weakly supervised person Re-ID: differentiable graphical learning and a new benchmark[EB/OL] . [2021-02-03]. https://arxiv.org/pdf/1904.03845v2.pdf https://arxiv.org/pdf/1904.03845v2.pdf

Wang G R, Wang G C, Zhang X J, Lai J H and Lin L. 2019c. Weakly supervised person re-identification: cost-effective learning with a new benchmark[EB/OL]. [2021-02-02] . https://arxiv.org/pdf/1904.03845.pdf https://arxiv.org/pdf/1904.03845.pdf

Wang G X, Kang W X, Wu Q X, Wang Z Y and Gao J B. 2018b. Generative adversarial network (GAN) based data augmentation for palmprint recognition//Proceedings of 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra, Australia: IEEE: 1-7[ DOI: 10.1109/DICTA.2018.8615782 http://dx.doi.org/10.1109/DICTA.2018.8615782 ]

Wang H, Wang Y T, Zhou Z, Ji X, Gong D H, Zhou J C, Li Z F and Liu W. 2018c. CosFace: large margin cosine loss for deep face recognition//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5265-5274[ DOI: 10.1109/CVPR.2018.00552 http://dx.doi.org/10.1109/CVPR.2018.00552 ]

Wang J and Wang G Q. 2017. Quality-specific hand vein recognition system. IEEE Transactions on Information Forensics and Security, 12(11): 2599-2610[DOI:10.1109/TIFS.2017.2713340]

Wang J D, Sun K, Cheng T H, Jiang B R, Deng C R, Zhao Y, Liu D, Mu Y D, Tan M K, Wang X G, Liu W Y and Xiao B. 2020f. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-99[DOI:10.1109/TPAMI.2020.2983686]

Wang J G, Yau W Y and Suwandy A. 2008a. Feature-level fusion of palmprint and palm vein for person identification based on a "Junction Point" representation//Proceedings of the 15th IEEE International Conference on Image Processing. San Diego, USA: IEEE: 253-256[ DOI: 10.1109/ICIP.2008.4711739 http://dx.doi.org/10.1109/ICIP.2008.4711739 ]

Wang J G, Yau W Y, Suwandy A and Sung E. 2007a. Fusion of palmprint and palm vein images for person recognition based on "laplacianpalm" feature//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE: 1-8[ DOI: 10.1109/CVPR.2007.383386 http://dx.doi.org/10.1109/CVPR.2007.383386 ]

Wang J G, Yau W Y, Suwandy A and Sung E. 2008b. Person recognition by fusing palmprint and palm vein images based on "Laplacianpalm" representation. Pattern Recognition, 41(5): 1514-1527[DOI:10.1016/j.patcog.2007.10.021]

Wang J, Wang G Q and Zhou M. 2018d. Bimodal vein data mining via cross-selected-domain knowledge transfer. IEEE Transactions on Information Forensics and Security, 13(3): 733-744[DOI:10.1109/TIFS.2017.2766039]

Wang K and Kumar A. 2019. Toward more accurate iris recognition using dilated residual features. IEEE Transactions on Information Forensics and Security, 14(12): 3233-3245[DOI:10.1109/TIFS.2019.2913234]

Wang K J and Yuan Z. 2007. Finger vein recognition based on wavelet moment fused with PCA transform. Pattern Recognition and Artificial Intelligence, 20(5): 692-697

王科俊, 袁智. 2007. 基于小波矩融合PCA变换的手指静脉识别. 模式识别与人工智能, 20(5): 692-697[DOI:10.3969/j.issn.1003-6059.2007.05.018]

Wang L L, Wang J, Li L T, Zheng T F and Soong F K. 2016. Improving speaker verification performance against long-term speaker variability. Speech Communication, 79: 14-29[DOI:10.1016/j.specom.2016.02.004]

Wang L Y, Leedham G and Cho D S Y. 2008c. Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recognition, 41(3): 920-929[DOI:10.1016/j.patcog.2007.07.012]

Wang L Y, Zhang K B, Ren M, Wang Y L and Sun Z N. 2020g. Recognition oriented iris image quality assessment in the feature space//Proceedings of 2020 International Joint Conference on Biometrics. Houston, USA: IEEE: 1-9[ DOI: 10.1109/IJCB48548.2020.9304896 http://dx.doi.org/10.1109/IJCB48548.2020.9304896 ]

Wang L, Tan T N, Hu W M and Ning H Z. 2003a. Automatic gait recognition based on statistical shape analysis. IEEE Transactions on Image Processing, 12(9): 1120-1131[DOI:10.1109/TIP.2003.815251]

Wang L, Tan T N, Ning H Z and Hu W M. 2003b. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12): 1505-1518[DOI:10.1109/TPAMI.2003.1251144]

Wang P and Qin H F. 2019. Palm-vein verification based on U-Net. IOP Conference Series: Materials Science and Engineering, 806: 012043[DOI:10.1088/1757-899X/806/1/012043]

Wang Q, Lee K A and Koshinaka T. 2020h. Using multi-resolution feature maps with convolutional neural networks for anti-spoofing in ASV//Proceedings of the Odyssey 2020: The Speaker and Language Recognition Workshop. Tokyo, Japan: [s. n.]: 138-142

Wang R, Wang G Y, Chen Z, Zeng Z G and Wang Y. 2014a. A palm vein identification system based on Gabor wavelet features. Neural Computing and Applications, 24(1): 161-168[DOI:10.1007/s00521-013-1514-8]

Wang R, Xu J F, Ma L, Xie X F, Huang Y H, Wang J and Liu Y. 2019e. FakeSpotter: a simple yet robust baseline for spotting AI-synthesized fake faces[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1909.06122.pdf https://arxiv.org/pdf/1909.06122.pdf

Wang S Y, Wang O, Owens A, Zhang R and Efros A A. 2019f. Detecting photoshopped faces by scripting photoshop[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1906.05856v1.pdf https://arxiv.org/pdf/1906.05856v1.pdf

Wang T C, Liu M Y, Tao A, Liu G L, Kautz J and Catanzaro B. 2019g. Few-shot video-to-video synthesis[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1910.12713v1.pdf https://arxiv.org/pdf/1910.12713v1.pdf

Wang T, Yang J W, Lei Z, Liao S C and Li S Z. 2013. Face liveness detection using 3D structure recovered from a single camera//Proceedings of 2013 International Conference on Biometrics (ICB). Madrid, Spain: IEEE: 1-6[ DOI: 10.1109/ICB.2013.6612957 http://dx.doi.org/10.1109/ICB.2013.6612957 ]

Wang X Y, Bo L F and Li F X. 2019h. Adaptive wing loss for robust face alignment via heatmap regression//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 6970-6980[ DOI: 10.1109/ICCV.2019.00707 http://dx.doi.org/10.1109/ICCV.2019.00707 ]

Wang X, Thome N and Cord M. 2017b. Gaze latent support vector machine for image classification improved by weakly supervised region selection. Pattern Recognition, 72: 59-71[DOI:10.1016/j.patcog.2017.07.001]

Wang Y C, Chen Z Z, Wu F and Wang G. 2018e. Person re-identification with cascaded pairwise convolutions//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 1470-1478[ DOI: 10.1109/CVPR.2018.00159 http://dx.doi.org/10.1109/CVPR.2018.00159 ]

Wang Y C, Hassebrook L G and Lau D L. 2010a. Data acquisition and processing of 3-D fingerprints. IEEE Transactions on Information Forensics and Security, 5(4): 750-760[DOI:10.1109/TIFS.2010.2062177]

Wang Y D, Li K F and Cui J L. 2010b. Hand-dorsa vein recognition based on partition local binary pattern//Proceedings of 2010 IEEE 10th International Conference on Signal Processing Proceedings. Beijing, China: IEEE: 1671-1674[ DOI: 10.1109/ICOSP.2010.5656717 http://dx.doi.org/10.1109/ICOSP.2010.5656717 ]

Wang Y D, Zhang K and Shark L K. 2014b. Personal identification based on multiple keypoint sets of dorsal hand vein images. IET Biometrics, 3(4): 234-245[DOI:10.1049/iet-bmt.2013.0042]

Wang Y H, Tan T N and Jain A K. 2003c. Combining face and iris biometrics for identity verification//Proceedings of International Conference on Audio- and Video-BasedBiometric Person Authentication. Guildford, UK: Springer: 805-813[ DOI: 10.1007/3-540-44887-X_93 http://dx.doi.org/10.1007/3-540-44887-X_93 ]

Wang Y N, Liao S C and Shao L. 2020i. Surpassing real-world source training data: random 3D characters for generalizable person re-identification//Proceedings of the 28th ACM International Conference on Multimedia. Seattle, USA: ACM: 3422-3430[ DOI: 10.1145/3394171.3413815 http://dx.doi.org/10.1145/3394171.3413815 ]

Wang Y X, Skerry-Ryan R J, Stanton D, Wu Y H, Weiss R J, Jaitly N, Yang Z H, Xiao Y, Chen Z F, Bengio S, Le Q, Agiomyrgiannakis Y, Clark R and Saurous R A. 2017c. Tacotron: towards end-to-end speech synthesis[EB/OL]. [2021-02-03] . http://export.arxiv.org/pdf/1703.10135.pdf http://export.arxiv.org/pdf/1703.10135.pdf

Wei J Z, Wang Y L, Wu X, He Z F, He R and Sun Z N. 2019. Cross-sensor iris recognition using adversarial strategy and sensor-specific information//Proceedings of the 10th IEEE International Conference on Biometrics Theory, Applications, and Systems. Tampa, USA: IEEE: 1-8[ DOI: 10.1109/BTAS46853.2019.9186008 http://dx.doi.org/10.1109/BTAS46853.2019.9186008 ]

Wei L H, Zhang S L, Gao W and Tian Q. 2018. Person transfer GAN to bridge domain gap for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 79-88[ DOI: 10.1109/CVPR.2018.00016 http://dx.doi.org/10.1109/CVPR.2018.00016 ]

Wei Z S, Tan T N, Sun Z N and Cui J L. 2005. Robust and fast assessment of iris image quality//Proceedings of International Conference on Biometrics. Berlin, Germany: Springer: 464-471[ DOI: 10.1007/11608288_62 http://dx.doi.org/10.1007/11608288_62 ]

Weinberger K Q and Saul L K. 2009. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10: 207-244

Wen D, Han H and Jain A K.2015. Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 10(4): 746-761[DOI:10.1109/TIFS.2015.2400395]

Wen Y D, Zhang K P, Li Z F and Qiao Y. 2016. A discriminative feature learning approach for deep face recognition//Proceedings of European Conference on Computer Vision. Amsterdam, the Netherlands: Springer: 499-515[ DOI: 10.1007/978-3-319-46478-7_31 http://dx.doi.org/10.1007/978-3-319-46478-7_31 ]

Whitelam C, Taborsky E, Blanton A, Maze B, Adams J, Miller T, Kalka N, Jain A K, Duncan J A, Allen K, Cheney J and Grother P. 2017. IARPA Janus benchmark-B face dataset//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, USA: IEEE: 592-600[ DOI: 10.1109/CVPRW.2017.87 http://dx.doi.org/10.1109/CVPRW.2017.87 ]

Wilburn B, Joshi N, Vaish V, Talvala E V, Antunez E, Barth A, Adams A, Horowitz M and Levoy M. 2005. High performance imaging using large camera arrays//Proceedings of the ACM SIGGRAPH. Los Angeles, USA: ACM: 765-776[ DOI: 10.1145/1186822.1073259 http://dx.doi.org/10.1145/1186822.1073259 ]

Wildes R P. 1997. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9): 1348-1363[DOI:10.1109/5.628669]

Wiles O, Koepke A S and Zisserman A. 2018. Self-supervised learning of a facial attribute embedding from video[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1808.06882.pdf https://arxiv.org/pdf/1808.06882.pdf

Wolf T, Babaee M and Rigoll G. 2016. Multi-view gait recognition using 3D convolutional neural networks//Proceedings of 2006 IEEE International Conference on Image Processing (ICIP). Phoenix, USA: IEEE: 4165-4169[ DOI: 10.1109/ICIP.2016.7533144 http://dx.doi.org/10.1109/ICIP.2016.7533144 ]

Wright J, Yang A Y, Ganesh A, Sastry S S and Ma Y. 2009. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2): 210-227[DOI:10.1109/TPAMI.2008.79]

Wu A C, Zheng W S and Lai J H. 2017a. Robust depth-based person re-identification. IEEE Transactions on Image Processing, 26(6): 2588-2603[DOI:10.1109/TIP.2017.2675201]

Wu A C, Zheng W S and Lai J H. 2019. Unsupervised person re-identification by camera-aware similarity consistency learning//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 6921-6930[ DOI: 10.1109/ICCV.2019.00702 http://dx.doi.org/10.1109/ICCV.2019.00702 ]

Wu A C, Zheng W S, Gong S G and Lai J H. 2020a. RGB-IR person re-identification by cross-modality similarity preservation. International Journal of Computer Vision, 128: 1765-1785[DOI:10.1007/s11263-019-01290-1]

Wu D, Pigou L, Kindermans P J, Le N D H, Shao L, Dambre J and Odobez J M. 2016a. Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8): 1583-1597[DOI:10.1109/TPAMI.2016.2537340]

Wu H B, Liu A T and Lee H Y. 2020b. Defense for black-box attacks on anti-spoofing models by self-supervised learning[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2006.03214.pdf https://arxiv.org/pdf/2006.03214.pdf

Wu J D and Liu C T. 2011. Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Systems with Applications, 38(5): 5423-5427[DOI:10.1016/j.eswa.2010.10.013]

Wu K S, Lee J C, Lo T M, Chang K C and Chang C P. 2013. A secure palm vein recognition system. Journal of Systems and Software, 86(11): 2870-2876[DOI:10.1016/j.jss.2013.06.065]

Wu L F, Ma Y K, Zhou P and Zheng W S. 2016. Review of biometric template protection. Chinese Journal of Scientific Instrument, 37(11): 2407-2420

毋立芳, 马玉琨, 周鹏, 郑伟诗. 2016. 生物特征模板保护综述. 仪器仪表学报, 37(11): 2407-2420[DOI:10.3969/j.issn.0254-3087.2016.11.001]

Wu L, Shen C H and Hengel A V D. 2016b. Personnet: Person Re-Identification with Deep Convolutional Neural Networks[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1601.07255.pdf https://arxiv.org/pdf/1601.07255.pdf

Wu W Y, Qian C, Yang S, Wang Q, Cai Y C and Zhou Q. 2018a. Look at boundary: a boundary-aware face alignment algorithm//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 2129-2138[ DOI: 10.1109/CVPR.2018.00227 http://dx.doi.org/10.1109/CVPR.2018.00227 ]

Wu W, Zhang Y X, Li C, Qian C and Loy C C. 2018b. ReenactGAN: learning to reenact faces via boundary transfer//Proceedings of European Conference on Computer Vision. Munich, Germany: Springer: 622-638[ DOI: 10.1007/978-3-030-01246-5_37 http://dx.doi.org/10.1007/978-3-030-01246-5_37 ]

Wu X Q, Zhang D and Wang K Q. 2003. Fisherpalms based palmprint recognition. Pattern Recognition Letters, 24(15): 2829-2838[DOI:10.1016/S0167-8655(03)00141-7]

Wu X, He R, Sun Z N and Tan T N. 2018c. A light CNN for deep face representation with noisy labels. IEEETransactions on Information Forensics and Security, 13(11): 2884-2896[DOI:10.1109/TIFS.2018.2833032]

Wu Y, Lin Y T, Dong X Y, Yan Y, Ouyang W L and Yang Y. 2018d. Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5177-5186[ DOI: 10.1109/CVPR.2018.00543 http://dx.doi.org/10.1109/CVPR.2018.00543 ]

Wu Z F, Huang Y Z, Wang L, Wang X G and Tan T N. 2017b. A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(2): 209-226[DOI:10.1109/TPAMI.2016.2545669]

Wu Z H and Yang Y C. 2009. Models and Methods for Speaker Recognition. Beijing: Tsinghua University Press

吴朝晖, 杨莹春. 2009. 说话人识别模型与方法. 北京: 清华大学出版社

Xiao L H, Sun Z N, He R and Tan T N. 2013a. Coupled feature selection for cross-sensor iris recognition//Proceedings of 2013 IEEE 6th International Conference on Biometrics: Theory, Applications and Systems (BTAS). Arlington, USA: IEEE: 1-6[ DOI: 10.1109/BTAS.2013.6712752 http://dx.doi.org/10.1109/BTAS.2013.6712752 ]

Xiao L H, Sun Z N, He R and Tan T N. 2013b. Margin based feature selection for cross-sensor iris recognition via linear programming//Proceedings of the 2nd IAPR Asian Conference on Pattern Recognition. Naha, Japan: IEEE: 246-250[ DOI: 10.1109/ACPR.2013.34 http://dx.doi.org/10.1109/ACPR.2013.34 ]

Xiao X, Tian X H, Du S, Xu H H, Chng E S and Li H Z. 2015. Spoofing speech detection using high dimensional magnitude and phase features: the NTU approach for ASVspoof 2015 challenge//Proceedings of the INTERSPEECH 2015. Dresden, Germany: [s. n.]: 2052-2056

Xie C H and Kumar A. 2019. Finger vein identification using convolutional neural network and supervised discrete hashing. Pattern Recognition Letters, 119: 148-156[DOI:10.1016/j.patrec.2017.12.001]

Xiong X H and de la Torre F. 2013. Supervised descent method and its applications to face alignment//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: 532-539[ DOI: 10.1109/CVPR.2013.75 http://dx.doi.org/10.1109/CVPR.2013.75 ]

Xu B Q, He L X, Liao X Y, Liu W, Sun Z N and Mei T. 2020. Black re-ID: a head-shoulder descriptor for the challenging problem of person re-identification//Proceedings of the 28th ACM International Conference on Multimedia. Seattle, USA: ACM: 673-681[ DOI: 10.1145/3394171.3414056 http://dx.doi.org/10.1145/3394171.3414056 ]

Xu R Z, Zhou Z M, Zhang W N and Yong Y. 2017. Face transfer with generative adversarial network[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1710.06090.pdf https://arxiv.org/pdf/1710.06090.pdf

Yadav S, Chen C J and Ross A. 2019. Synthesizing iris images using RaSGAN with application in presentation attack detection//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, USA: IEEE: 2422-2430[ DOI: 10.1109/CVPRW.2019.00297 http://dx.doi.org/10.1109/CVPRW.2019.00297 ]

Yam C Y, Nixon M S and Carter J N. 2002. Gait recognition by walking and running: a model-based approach//Proceedings of the 5th Asian Conference on Computer Vision. Melbourne, Australia: [s. n.]: 1-6

Yam C Y, Nixon M S and Carter J N. 2004. Automated person recognition by walking and running via model-based approaches. Pattern Recognition, 37(5): 1057-1072[DOI:10.1016/j.patcog.2003.09.012]

Yaman D, Eyiokur F I and Ekenel H K. 2019. Multimodal age and gender classification using ear and profile face images//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, USA: IEEE: 2414-2421[ DOI: 10.1109/CVPRW.2019.00296 http://dx.doi.org/10.1109/CVPRW.2019.00296 ]

Yambay D, Becker B, Kohli N, Yadav D, Czajka A, Bowyer K W, Schuckers S, Singh R, Vatsa M, Noore A, Grananiello D, Sansone C, Verdoliva L, He L X, Ru Y W, Li H Q, Liu N F, Sun Z N and Tan T N. 2017. LivDet iris 2017-Iris liveness detection competition 2017//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 733-741[ DOI: 10.1109/BTAS.2017.8272763 http://dx.doi.org/10.1109/BTAS.2017.8272763 ]

Yan X K, Kang W X, Deng F Q and Wu Q X. 2015. Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing, 151: 798-807[DOI:10.1016/j.neucom.2014.10.019]

Yang A Y, Zhou Z H, Balasubramanian A G, Sastry S S and Ma Y. 2013. Fast ℓ1-minimization algorithms for robust face recognition. IEEE Transactions on Image Processing, 22(8): 3234-3246[DOI:10.1109/TIP.2013.2262292]

Yang B and Martiri E. 2015. Using honey templates to augment hash based biometric template protection//Proceedings of the 39th IEEE Annual Computer Software and Applications Conference. Taichung, China: IEEE: 312-316[ DOI: 10.1109/COMPSAC.2015.247 http://dx.doi.org/10.1109/COMPSAC.2015.247 ]

Yang F X, Zhong Z, Luo Z M, Lian S and Li S Z. 2020a. Leveraging virtual and real person for unsupervised person re-identification. IEEE Transactions on Multimedia, 22(9): 2444-2453[DOI:10.1109/TMM.2019.2957928]

Yang J F and Shi Y H. 2012. Finger-vein ROI localizationand vein ridge enhancement. Pattern Recognition Letters, 33(12): 1569-1579[DOI:10.1016/j.patrec.2012.04.018]

Yang J F and Yang J L. 2009. Multi-channel Gabor filter design for finger-vein image enhancement//Proceedings of the 5th International Conference on Image and Graphics. Xi'an, China: IEEE: 87-91[ DOI: 10.1109/ICIG.2009.170 http://dx.doi.org/10.1109/ICIG.2009.170 ]

Yang J F, Zhang B and Shi Y H. 2012a. Scattering removal for finger-vein image restoration. Sensors, 12(3): 3627-3640[DOI:10.3390/s120303627]

Yang J R, Zheng W S, Yang Q Z, Chen Y C and Tian Q. 2020b. Spatial-temporal graph convolutional network for video-based person re-identification//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3286-3296[ DOI: 10.1109/CVPR42600.2020.00335 http://dx.doi.org/10.1109/CVPR42600.2020.00335 ]

YangJ W, Lei Z and Li S Z. 2014a. Learn convolutional neural network for face anti-spoofing[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1408.5601v2.pdf https://arxiv.org/pdf/1408.5601v2.pdf

Yang J, Liu Q S and Zhang K H. 2017. Stacked hourglass network for robust facial landmark localisation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, USA: IEEE: 2025-2033[ DOI: 10.1109/CVPRW.2017.253 http://dx.doi.org/10.1109/CVPRW.2017.253 ]

Yang L, Yang G P, Xi X M, Su K, Chen Q and Yin Y L. 2019a. Finger vein code: from indexing to matching. IEEE Transactions on Information Forensics and Security, 14(5): 1210-1223[DOI:10.1109/TIFS.2018.2871778]

Yang M, Zhang L and Zhang D. 2012b. Efficient misalignment-robust representation for real-time face recognition//Proceedings of European Conference on Computer Vision. Florence, Italy: Springer: 850-863[ DOI: 10.1007/978-3-642-33718-5_61 http://dx.doi.org/10.1007/978-3-642-33718-5_61 ]

Yang Q Z, Wu A C and Zheng W S. 2019b. Person re-identification by contour sketch under moderate clothing change. IEEE Transactions on Pattern Analysis and Machine Intelligence: #2960509[DOI:10.1109/TPAMI.2019.2960509]

Yang Q Z, Yu H X, Wu A C and Zheng W S. 2019c. Patch-based discriminative feature learning for unsupervised person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 3628-3637[ DOI: 10.1109/CVPR.2019.00375 http://dx.doi.org/10.1109/CVPR.2019.00375 ]

Yang S, Luo P, Loy C C and Tang X O. 2016. Wider face: a face detection benchmark//Proceedings of 2016 IEEE Conference on ComputerVision and Pattern Recognition. Las Vegas, USA: IEEE: 5525-5533[ DOI: 10.1109/CVPR.2016.596 http://dx.doi.org/10.1109/CVPR.2016.596 ]

Yang W L, Luo W, Kang W X, Huang Z X and Wu Q X. 2020c. FVRAS-Net: an embedded finger-vein recognition and AntiSpoofing system using a unified CNN. IEEE Transactions on Instrumentation and Measurement, 69(11): 8690-8701[DOI:10.1109/TIM.2020.3001410]

Yang W M, Huang X L, Zhou F and Liao Q M. 2014b. Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Information Sciences, 268: 20-32[DOI:10.1016/j.ins.2013.10.010]

Yang X F and Sun D M. 2017. Feature-level fusion of palmprint and palm vein base on canonical correlation analysis//Proceedings of the 13th IEEE International Conference on Signal Processing. Chengdu, China: IEEE: 1353-1356[ DOI: 10.1109/ICSP.2016.7878047 http://dx.doi.org/10.1109/ICSP.2016.7878047 ]

Yang X, Feng J J and Zhou J. 2014c. Localized dictionaries based orientation field estimation for latent fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5): 955-969[DOI:10.1109/TPAMI.2013.184]

Yang X, Li Y Z and Lyu S W. 2019d. Exposing deep fakes using inconsistent head poses//Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE: 8261-8265[ DOI: 10.1109/ICASSP.2019.8683164 http://dx.doi.org/10.1109/ICASSP.2019.8683164 ]

Yang X, Luo W H, Bao L C, Gao Y, Gong D H, Zheng S B, Li Z F and Liu W. 2019e. Face anti-spoofing: model matters, so does data//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 3502-3511[ DOI: 10.1109/CVPR.2019.00362 http://dx.doi.org/10.1109/CVPR.2019.00362 ]

Yang X, Wei F Y, Zhang H Y and Zhu J. 2019f. Design and interpretation of universal adversarial patches in face detection[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1912.05021.pdf https://arxiv.org/pdf/1912.05021.pdf

Yang X, Yang D C, Dong Y P, Yu W J, Su H and Zhu J. 2020d. Delving into the adversarial robustness on face recognition[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2007.04118.pdf https://arxiv.org/pdf/2007.04118.pdf

Yao A C. 1982. Protocols for secure computations//Proceedings of the 23rd Annual Symposium on Foundations of Computer Science. Chicago, USA: IEEE: 160-164[ DOI: 10.1109/SFCS.1982.38 http://dx.doi.org/10.1109/SFCS.1982.38 ]

Ye M, Ma A J, Zheng L, Li J W and Yuen P C. 2017. Dynamic label graph matching for unsupervised video re-identification//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 5152-5160[ DOI: 10.1109/ICCV.2017.550 http://dx.doi.org/10.1109/ICCV.2017.550 ]

Yi D, Lei Z, Liao S C and Li S Z. 2014. Learning face representation from scratch[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1411.7923.pdf https://arxiv.org/pdf/1411.7923.pdf

Yin J H, Wu A C and Zheng W S. 2020. Fine-grained person re-identification. International Journal of Computer Vision, 128(6): 1654-1672[DOI:10.1007/s11263-019-01259-0]

Yin Y L, Liu L L and Sun X W. 2011. SDUMLA-HMT: a multimodal biometric database//Proceedings of Chinese Conference on Biometric Recognition. Beijing, China: Springer: 260-268[ DOI: 10.1007/978-3-642-25449-9_33 http://dx.doi.org/10.1007/978-3-642-25449-9_33 ]

Yin Z, Zheng W S, Wu A C, Yu H X, Wan H, Guo X W, Huang F Y and Lai J H. 2018. Adversarial attribute-image person re-identification//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: AAAI Press: 1100-1106[ DOI: 10.24963/ijcai.2018/153 http://dx.doi.org/10.24963/ijcai.2018/153 ]

Yoo J H, Nixon M S and Harris C J. 2002a. Extracting human gait signatures by body segment properties//Proceedings of the 5th IEEE Southwest Symposium on Image Analysis and Interpretation. Sante Fe, USA: IEEE: 35-39[ DOI: 10.1109/IAI.2002.999885 http://dx.doi.org/10.1109/IAI.2002.999885 ]

Yoo J H, Nixon M S and Harris C J. 2002b. Model-driven statistical analysis of human gait motion//Proceedings of the International Conference on Image Processing. Rochester, USA: IEEE: I-285-I-288[ DOI: 10.1109/ICIP.2002.1038015 http://dx.doi.org/10.1109/ICIP.2002.1038015 ]

Yu H X and Zheng W S. 2020. Weakly supervised discriminative feature learning with state information for person identification//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 5527-5537[ DOI: 10.1109/CVPR42600.2020.00557 http://dx.doi.org/10.1109/CVPR42600.2020.00557 ]

Yu H X, Wu A C and Zheng W S. 2017a. Cross-view asymmetric metric learning for unsupervised person re-identification//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 994-1002[ DOI: 10.1109/iccv.2017.113 http://dx.doi.org/10.1109/iccv.2017.113 ]

Yu H X, Wu A C and Zheng W S. 2020a. Unsupervised person re-identification by deep asymmetric metric embedding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4): 956-973[DOI:10.1109/TPAMI.2018.2886878]

Yu H X, Zheng W S, Wu A C, Guo X W, Gong S G and Lai J H. 2019a. Unsupervised person re-identification by soft multilabel learning//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 2143-2152[ DOI: 10.1109/CVPR.2019.00225 http://dx.doi.org/10.1109/CVPR.2019.00225 ]

Yu S J, Li S H, Chen D P, Zhao R, Yan J J and Qiao Y. 2020b. COCAS: a large-scale clothes changing person dataset for re-identification//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3397-3406[ DOI: 10.1109/CVPR42600.2020.00346 http://dx.doi.org/10.1109/CVPR42600.2020.00346 ]

Yu S Q, Chen H F, Reyes E B G and Poh N. 2017b. GaitGAN: invariant gait feature extraction using generative adversarial networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, USA: IEEE: 532-539[ DOI: 10.1109/CVPRW.2017.80 http://dx.doi.org/10.1109/CVPRW.2017.80 ]

Yu S Q, Chen H F, Wang Q, Shen L L and Huang Y Z. 2017c. Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing, 239: 81-93[DOI:10.1016/j.neucom.2017.02.006]

Yu S Q, Tan D L and Tan T N. 2006. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition//Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE: 441-444[ DOI: 10.1109/ICPR.2006.67 http://dx.doi.org/10.1109/ICPR.2006.67 ]

Yu S Q, Wang L, Hu W M and Tan T N. 2004. Gait analysis for human identification in frequency domain//Proceedings of the 3rd International Conference on Image and Graphics. Hong Kong, China: IEEE: 282-285[ DOI: 10.1109/ICIG.2004.72 http://dx.doi.org/10.1109/ICIG.2004.72 ]

Yu S Q, Wang Q and Huang Y Z. 2013. A large RGB-D gait dataset and the baseline algorithm//Proceedings of Chinese Conference on Biometric Recognition. Jinan, China: Springer: 417-424[ DOI: 10.1007/978-3-319-02961-0_52 http://dx.doi.org/10.1007/978-3-319-02961-0_52 ]

Yu Z T, Peng W, Li X B, Hong X P and Zhao G Y. 2019b. Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE: 151-160[ DOI: 10.1109/ICCV.2019.00024 http://dx.doi.org/10.1109/ICCV.2019.00024 ]

Yuksel A, Akarun L and Sankur B. 2011. Hand vein biometry based on geometry and appearance methods. IET Computer Vision, 5(6): 398-406[DOI:10.1049/iet-cvi.2010.0175]

Zhang B, Li J, Wang Y B, Tai Y, Wang C J, Li J L, Huang F Y, Xia Y L, Pei W J and Ji R R. 2020a. ASFD: automatic and scalable face detector[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2003.11228.pdf https://arxiv.org/pdf/2003.11228.pdf

Zhang C Y, Wu L and Wang Y. 2019a. Crossing generative adversarial networks for cross-view person re-identification. Neurocomputing, 340: 259-269[DOI:10.1016/j.neucom.2019.01.093]

Zhang C, Hou G Q, Zhang Z X, Sun Z N and Tan T N. 2018a. Efficient auto-refocusing for light field camera. Pattern Recognition, 81: 176-189[DOI:10.1016/j.patcog.2018.03.020]

Zhang D P and Shu W. 1999. Two novel characteristics in palmprint verification: datum point invariance and line feature matching. Pattern Recognition, 32(4): 691-702[DOI:10.1016/S0031-3203(98)00117-4]

Zhang D, Guo Z H, Lu G M, Zhang L and Zuo W M. 2010a. An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement, 59(2): 480-490[DOI:10.1109/TIM.2009.2028772]

Zhang D, Kong W K, You J and Wong M. 2003. Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9): 1041-1050[DOI:10.1109/TPAMI.2003.1227981]

Zhang D, Zuo W M and Yue F. 2012. A comparative study of palmprint recognition algorithms. ACM Computing Surveys, 44(1): #2[DOI:10.1145/2071389.2071391]

Zhang E H, Zhao Y W and Xiong W. 2010b. Active energy image plus 2DLPP for gait recognition. Signal Processing, 90(7): 2295-2302[DOI:10.1016/j.sigpro.2010.01.024]

Zhang H, Goodfellow I, Metaxas D and Odena A. 2019b. Self-attention generative adversarial networks//Proceedings of the 36th International Conference on Machine Learning. Long Beach, USA: [s. n.]: 7354-7363

Zhang H, Sun Z N, Tan T N and Wang J Y. 2011. Learning hierarchical visual codebook for iris liveness detection//Proceedings of the International Joint Conference on Biometrics. Washington D C, USA: IEEE: 1-6

Zhang J and Yang J F. 2009. Finger-vein image enhancement based on combination of gray-level grouping and circular Gabor filter//Proceedings of 2009 International Conference on Information Engineering and Computer Science. Wuhan, China: IEEE: 1-4[ DOI: 10.1109/ICIECS.2009.5363431 http://dx.doi.org/10.1109/ICIECS.2009.5363431 ]

Zhang J, Shan S G, Kan M N and Chen X L. 2014a. Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment//Proceedings of European Conference on Computer Vision. Zurich, Switzerland: Springer: 1-16[ DOI: 10.1007/978-3-319-10605-2_1 http://dx.doi.org/10.1007/978-3-319-10605-2_1 ]

Zhang K B, Shen Z T, Wang Y L and Sun Z N. 2020b. All-in-focus iris camera with a great capture volume//Proceedings of 2020 IEEE International Joint Conference on Biometrics (IJCB). Houston, USA: IEEE: 1-9[ DOI: 10.1109/IJCB48548.2020.9304932 http://dx.doi.org/10.1109/IJCB48548.2020.9304932 ]

Zhang K P, Zhang Z P, Li Z F and Qiao Y. 2016. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10): 1499-1503[DOI:10.1109/LSP.2016.2603342]

Zhang L H, Tan S and Yang J. 2017a. Hearing your voice is not enough: an articulatory gesture based liveness detection for voice authentication//Proceedings of 2017 ACM SIGSAC Conference on Computer and Communications Security. Dallas Texas, USA: ACM: 57-71[ DOI: 10.1145/3133956.3133962 http://dx.doi.org/10.1145/3133956.3133962 ]

Zhang L, Cheng Z X, Shen Y and Wang D Q. 2018b. Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset. Symmetry, 10(4): 78[DOI:10.3390/sym10040078]

Zhang L, Li L D, Yang A Q, Shen Y and Yang M. 2017b. Towards contactless palmprint recognition: a novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recognition, 69: 199-212[DOI:10.1016/j.patcog.2017.04.016]

Zhang Q, Li H Q, Sun Z N and Tan T N. 2018c. Deep feature fusion for iris and periocular biometrics on mobile devices. IEEE Transactions on Information Forensics and Security, 13(11): 2897-2912[DOI:10.1109/TIFS.2018.2833033]

Zhang R, Vogler C and Metaxas D. 2007. Human gait recognition at sagittal plane. Image and Vision Computing, 25(3): 321-330[DOI:10.1016/j.imavis.2005.10.007]

Zhang S F, Zhu X Y, Lei Z, Shi H L, Wang X B and Li S Z. 2017c. FaceBoxes: a CPU real-time face detector with high accuracy//Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE: 1-9[ DOI: 10.1109/BTAS.2017.8272675 http://dx.doi.org/10.1109/BTAS.2017.8272675 ]

Zhang X, Karaman S and Chang S F. 2019c. Detecting and simulating artifacts in GAN fake images[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1907.06515.pdf https://arxiv.org/pdf/1907.06515.pdf

Zhang Y K, Jiang Z Y, Villalba J and Dehak N. 2020c. Black-box attacks on spoofing countermeasures using transferability of adversarial examples//Proceedings of the INTERSPEECH 2020. Shanghai, China: [s. n.]: 4238-4242[ DOI: 10.21437/Interspeech.2020-2834 http://dx.doi.org/10.21437/Interspeech.2020-2834 ]

Zhang Y L, Shi D Q, Zhan X S, Cao D, Zhu K Y and Li Z W. 2019d. Slim-resCNN: a deep residual convolutional neural network for fingerprint liveness detection. IEEE Access, 7: 91476-91487[DOI:10.1109/ACCESS.2019.2927357]

Zhang Y, Zheng L L and Thing V L L. 2017d. Automated face swapping and its detection//Proceedings of the 2nd International Conference on Signal and Image Processing (ICSIP). Singapore, Singapore: IEEE: 15-19[ DOI: 10.1109/SIPROCESS.2017.8124497 http://dx.doi.org/10.1109/SIPROCESS.2017.8124497 ]

Zhang Z H and Troje N F. 2005. View-independent person identification from human gait. Neurocomputing, 69(1/3): 250-256[DOI:10.1016/j.neucom.2005.06.002]

Zhang Z P, Luo P, Loy C C and Tang X O. 2014b. Faciallandmark detection by deep multi-task learning//Proceedings of European Conference on Computer Vision. Zurich, Switzerland: Springer: 94-108[ DOI: 10.1007/978-3-319-10599-4_7 http://dx.doi.org/10.1007/978-3-319-10599-4_7 ]

Zhang Z Y, Tran L, Yin X, Atoum Y, Liu X M, Wan J and Wang N X. 2019e. Gait recognition via disentangled representation learning//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 4705-4714[ DOI: 10.1109/CVPR.2019.00484 http://dx.doi.org/10.1109/CVPR.2019.00484 ]

Zhang Z Z, Lan C L, Zeng W J and Chen Z B. 2019f. Densely semantically aligned person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 667-676[ DOI: 10.1109/CVPR.2019.00076 http://dx.doi.org/10.1109/CVPR.2019.00076 ]

Zhao G Y, Liu G Y, Li H and Pietikainen M. 2006. 3D gait recognition using multiple cameras//Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE: 529-534[ DOI: 10.1109/FGR.2006.2 http://dx.doi.org/10.1109/FGR.2006.2 ]

Zhao J J, Tian H L, Xu W X and Li X. 2009. A new approach to hand vein image enhancement//Proceedings of the 2nd International Conference on Intelligent Computation Technology and Automation. Changsha, China: IEEE: 499-501[ DOI: 10.1109/ICICTA.2009.127 http://dx.doi.org/10.1109/ICICTA.2009.127 ]

Zhao S Z, Gao C X, Zhang J, Cheng H, Han C C, Jiang X Y, Guo X W, Zheng W S, Sang N and Sun X. 2020. Do not disturb me: person re-identification under the interference of other pedestrians//Proceedings of European Conference on Computer Vision. Glasgow, United Kingdom: Springer: 647-663[ DOI: 10.1007/978-3-030-58539-6_39 http://dx.doi.org/10.1007/978-3-030-58539-6_39 ]

Zhao Z J and Kumar A. 2017. Towards more accurate iris recognition using deeply learned spatially corresponding features//Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE: 3829-3838[ DOI: 10.1109/ICCV.2017.411 http://dx.doi.org/10.1109/ICCV.2017.411 ]

Zhao Z J and Kumar A. 2019. A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features. Pattern Recognition, 93: 546-557[DOI:10.1016/j.patcog.2019.04.010]

Zheng L L, Duffner S, Idrissi K, Garcia C and Baskurt A. 2016a. Siamese multi-layer perceptrons for dimensionality reduction and face identification. Multimedia Tools and Applications, 75(9): 5055-5073[DOI:10.1007/s11042-015-2847-3]

Zheng L, Shen L Y, Tian L, Wang S J, Wang J D and Tian Q. 2015a. Scalable person re-identification: a benchmark//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 1116-1124[ DOI: 10.1109/ICCV.2015.133 http://dx.doi.org/10.1109/ICCV.2015.133 ]

Zheng L, Zhang H H, Sun S Y, Chandraker M, Yang Y and Tian Q. 2017a. Person re-identification in the wild//Proceedings of 2017 IEEE International Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 3346-3355[ DOI: 10.1109/CVPR.2017.357 http://dx.doi.org/10.1109/CVPR.2017.357 ]

Zheng T F and Li L T. 2017. Robustness-Related Issues in Speaker Recognition. Singapore: Springer[DOI:10.1007/978-981-10-3238-7]

Zheng T F. 2020. To push on the systematism of trusted identity authentication technologies. Journal of Information Security Research, 6(7): #574

Zheng W S, Gong S G and Xiang T. 2013. Reidentification by relative distance comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3): 653-668[DOI:10.1109/TPAMI.2012.138]

Zheng W S, Gong S G and Xiang T. 2016b. Towards open-world person re-identification by one-shot group-based verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3): 591-606[DOI:10.1109/TPAMI.2015.2453984]

Zheng W S, Li X, Xiang T, Liao S C, Lai J H and Gong S G. 2015b. Partial person re-identification//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 4678-4686[ DOI: 10.1109/ICCV.2015.531 http://dx.doi.org/10.1109/ICCV.2015.531 ]

Zheng Z D, Yang X D, Yu Z D, Zheng L, Yang Y and Kautz J. 2019. Joint discriminative and generative learning for person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 2133-2142[ DOI: 10.1109/CVPR.2019.00224 http://dx.doi.org/10.1109/CVPR.2019.00224 ]

Zheng Z D, Zheng L and Yang Y. 2017b. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 3774-3782[ DOI: 10.1109/ICCV.2017.405 http://dx.doi.org/10.1109/ICCV.2017.405 ]

Zhong D X and Zhu J S. 2020. Centralized large margin cosine loss for open-set deep palmprint recognition. IEEE Transactions on Circuits and Systems for Video Technology, 30(6): 1559-1568[DOI:10.1109/TCSVT.2019.2904283]

Zhong D X, Shao H K and Du X F. 2019a. A hand-based multi-biometrics via deep hashing network and biometric graph matching. IEEE Transactions on Information Forensics and Security, 14(12): 3140-3150[DOI:10.1109/TIFS.2019.2912552]

Zhong D X, Yang Y and Du X F. 2018a. Palmprint recognition using siamese network//Proceedings of Chinese Conference on Biometric Recognition. Urumchi, China: Springer: 48-55[ DOI: 10.1007/978-3-319-97909-0_6 http://dx.doi.org/10.1007/978-3-319-97909-0_6 ]

Zhong Y Y and Deng W H. 2019. Adversarial learning with margin-based triplet embedding regularization//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 6548-6557[ DOI: 10.1109/ICCV.2019.00665 http://dx.doi.org/10.1109/ICCV.2019.00665 ]

Zhong Y Y and Deng W H. 2021. Towards transferable adversarial attack against deep face recognition. IEEE Transactions on Information Forensics and Security, 16: 1452-1466[DOI:10.1109/TIFS.2020.3036801]

Zhong Z, Zheng L, Luo Z M, Li S Z and Yang Y. 2019b. Invariance matters: exemplar memory for domain adaptive person re-identification//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 598-607[ DOI: 10.1109/CVPR.2019.00069 http://dx.doi.org/10.1109/CVPR.2019.00069 ]

Zhong Z, Zheng L, Zheng Z D, Li S Z and Yang Y. 2018b. Camera style adaptation for person re-identification//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5157-5166[ DOI: 10.1109/CVPR.2018.00541 http://dx.doi.org/10.1109/CVPR.2018.00541 ]

Zhou W B and Li N. 2019. Exploring implementation of three-dimensional skeleton reconstruction of finger vein for PPS. Procedia CIRP, 83: 826-830[DOI:10.1016/j.procir.2019.05.022]

Zhou Z, Tang D, Wang X F, Han W L, Liu X Y and Zhang K H. 2018. Invisible mask: practical attacks on face recognition with infrared[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/1803.04683.pdf https://arxiv.org/pdf/1803.04683.pdf

Zhu H, Fu C Y, Wu Q Y, Wu W, Qian C and He R. 2020a. AOT: appearance optimal transport based identity swapping for forgery detection[EB/OL]. [2021-02-03] . https://arxiv.org/pdf/2011.02674.pdf https://arxiv.org/pdf/2011.02674.pdf

Zhu J Y, Park T, Isola P and Efros A A. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2242-2251[ DOI: 10.1109/ICCV.2017.244 http://dx.doi.org/10.1109/ICCV.2017.244 ]

Zhu X Y, Lei Z, Liu X M, Shi H L and Li S Z. 2016. Face alignment across large poses: a 3D solution//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 146-155[ DOI: 10.1109/CVPR.2016.23 http://dx.doi.org/10.1109/CVPR.2016.23 ]

Zhuang L S, Yang A Y, Zhou Z H, Sastry S S and Ma Y. 2013. Single-sample face recognition with image corruption and misalignment via sparse illumination transfer//Proceedings of 2013 IEEE International Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE: 3546-3553[ DOI: 10.1109/CVPR.2013.455 http://dx.doi.org/10.1109/CVPR.2013.455 ]

Zhuo J X, Chen Z Y, Lai J H and Wang G C. 2018. Occluded person re-identification//Proceedings of 2018 IEEE International Conference on Multimedia and Expo (ICME). San Diego, USA: IEEE: 1-6[ DOI: 10.1109/ICME.2018.8486568 http://dx.doi.org/10.1109/ICME.2018.8486568 ]

Zuo J Y, Schmid N A and Chen X H. 2007. On generation and analysis of synthetic iris images. IEEE Transactions on Information Forensics and Security, 2(1): 77-90[DOI:10.1109/TIFS.2006.890305]

Zuo W M, Lin Z C, Guo Z H and Zhang D. 2010. The multiscale competitive code via sparse representation for palmprint verification//Proceedings of 2010 IEEE Computer Society Conference on ComputerVision and Pattern Recognition. San Francisco, USA: IEEE: 2265-2272[ DOI: 10.1109/CVPR.2010.5539909 http://dx.doi.org/10.1109/CVPR.2010.5539909 ]