浏览全部资源
扫码关注微信
纸质出版日期： 2020-08-16 ，
录用日期： 2020-01-15
移动端阅览
杨雪鹤, 刘欢喜, 肖建力. 多模态生物特征提取及相关性评价综述[J]. 中国图象图形学报, 2020,25(8):1529-1538.
Xuehe Yang, Huanxi Liu, Jianli Xiao. Extraction and relevance evaluation for multimodal biometric features[J]. Journal of Image and Graphics, 2020,25(8):1529-1538.
生物特征识别是身份认证的重要手段，特征提取技术在其中扮演了关键角色，直接影响识别的结果。随着特征提取技术日趋成熟，学者们逐渐将目光投向了生物特征间的相关性问题。本文以单模态和多模态生物识别中的特征提取方法为研究对象，回顾了人脸与指纹的特征提取方法，分析了基于经验知识的特征分类提取方法以及基于深度学习的计算机逻辑采样提取方法，并从图像处理的角度对单模态与多模态方法进行对比。以当前多模态生物特征提取方法和DNA表达过程为引，提出了不同模态的生物特征之间存在相关性的猜想，以及对这一猜想进行建模的思路。在多模态生物特征提取的基础上，对今后可能有进展的各生物特征之间的相关性建模进行了展望。
Biometrics
which is an important means of identity authentication
has been integrated into all aspects of daily life. The convenience and efficiency of single-modal biometrics and the reliability of multimodal biometrics have enabled the feature extraction technology to play a key role in directly affecting recognition results. As feature extraction techniques mature
researchers are turning their attention to the relevance of biometrics. In this research
the feature extraction methods in single-modal and multimodal biometrics are the object. We first review the feature extraction methods of face and fingerprint through the literature. The fingerprint feature extraction methods can roughly be divided into two categories. The first category calculates the fingerprint direction
which completes the estimation and judgment of the fingerprint local or the hole direction field. This method can be subdivided into three categories
which utilize gradient vector
filter
or mathematical model to build the fingerprint direction field. The second category targets the fingerprint pattern area
and the widely utilized methods are presented in this paper. Face feature extraction is based on the face representation process. Face representation can be divided into 2D-and 3D-based face representation methods according to different data represented by face. Pixels
including different color or points
are converted into feature vectors for different facial features that are invisible to the naked eye. In the traditional identification method
this recognition process relies on the accumulation of biometrics and recognition experience known to humans. A computer can learn and generalize when machine learning and deep learning are introduced. It can gradually overcome the cognitive deficit of humanity in face recognition and other fields. We analyze feature classification based on empirical knowledge and computer logic sampling extraction based on deep learning and operate these methods on single mode and multimode. The modeling of the correlation among biometrics that may progress in the future is explored on the basis of a comparison of multimodal biometrics. The knowledge gained in the field of computer science comes entirely from the natural evolution of our own or the Earth. Current results of single-modal and multimodal biometric technologies have saturated with the current requirements for identity verification applications. The high-efficiency and high-precision biofeature extraction method and the feature extraction requirements under the biorecognition framework are matched effectively. However
the study of the correlation among different biometrics remains blank. Such study is significant not only for image processing but also for many subdisciplines in the biological field. In this paper
we explain the feasibility of modeling the correlation among biometrics from the perspective of image processing. The assumption that biometrics can be converted into one another in the form of computer images is based on the following points:1) the origin of biometrics comes from DNA strands
which makes the characteristics of each individual possess mutualism and universality; 2) features obtained from images do not have the irreversibility of complex transformation processes
such as DNA to protein construction; 3) the aggregation
analysis
and coding of features can be realized on computers
and the vision of a computer gives it a stability that is far superior to that of human vision in the classification process. Biometric feature extraction methods based on single-modality and multimodality have been applied extensively. The current results of single-modal and multimodal biometric extraction techniques are reviewed
and the correlation between biometrics and their application prospects is determined.
生物特征提取指纹人脸多模态相关性
biometric feature extractionfingerprintfacemultimodalrelevance
Alkoot F M and Kittler J. 1999. Experimental evaluation of expert fusion strategies. Pattern Recognition Letters, 20(11/13):1361-1369[DOI:10.1016/s0167-8655(99)00107-5]
Bazen A M and Gerez S H. 2002. Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):905-919[DOI:10.1109/TPAMI.2002.1017618]
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]
Bharthi B, Kavitha S, Kotwal N P, Parasaram N and Piriyanga J. 2017. Unit selection based speech synthesis for converting short text message into voice message in mobile phones//Proceedings of the 4th IEEE International Conference on Advanced Computing and Communication Systems. Coimbatore, India: IEEE: 1-7[DOI: 10.1109/ICACCS.2017.8014603http://dx.doi.org/10.1109/ICACCS.2017.8014603]
Bian W X, Xu D Q, Li Q D, Cheng Y Q, Jie B and Ding X T. 2019. A survey of the methods on fingerprint orientation field estimation. IEEE Access, 7:32644-32663[DOI:10.1109/ACCESS.2019.2903601]
Cao Q, Shen L, Xie W D, Parkhi O M and Zisserman A. 2017. Vggface2: a dataset for recognizing faces across pose and age[EB/OL].[2019-05-13].https://arxiv.org/pdf/1710.08092.pdfhttps://arxiv.org/pdf/1710.08092.pdf
Chikkerur S and Ratha N. 2005. Impact of singular point detection on fingerprint matching performance//Proceedings of the 4th IEEE Workshop on Automatic Identification Advanced Technologies. Buffalo: IEEE: 207-212[DOI: 10.1109/AUTOID.2005.34http://dx.doi.org/10.1109/AUTOID.2005.34]
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, CA, USA: IEEE: 539-546[DOI: 10.1109/CVPR.2005.202http://dx.doi.org/10.1109/CVPR.2005.202]
Darlow L N and Rosman B. 2017. Fingerprint minutiae extraction using deep learning//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver, Colorado, USA: IEEE: 22-30[DOI: 10.1109/BTAS.2017.8272678http://dx.doi.org/10.1109/BTAS.2017.8272678]
Ding C X and Tao D C. 2016. A comprehensive survey on pose-invariant face recognition. ACM Transactions on Intelligent Systems and Technology, 7(3):#37[DOI:10.1145/2845089]
Fan H Q, Cao Z M, Jiang Y N, Yin Q and Doudou C. 2014. Learning deep face representation[EB/OL].[2019-09-20].https://arxiv.org/pdf/1403.2802v1.pdfhttps://arxiv.org/pdf/1403.2802v1.pdf
Gu J W, Zhou J and Zhang D. 2004. A combination model for orientation field of fingerprints. Pattern Recognition, 37(3):543-553[DOI:10.1016/s0031-3203(03)00178-x]
Guo B H, Nixon M S and Carter J N. 2017. Supervised generalized canonical correlation analysis of soft biometrie fusion for recognition at a distance//Proceedings of the 8th International Conference on Imaging for Crime Detection and Prevention. Madrid: IEEE: 49-54[DOI: 10.1049/ic.2017.0048http://dx.doi.org/10.1049/ic.2017.0048]
Harmon L D, Khan M K, Lasch R and Ramig P F. 1981. Machine identification of human faces. Pattern Recognition, 13(2):97-110[DOI:10.1016/0031-3203(81)90008-x]
Jain A K, Prabhakar S, Hong L and Pankanti S. 2000. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, 9(5):846-859[DOI:10.1109/83.841531]
Jain A K and Ross A. 2002. Learning user-specific parameters in a multibiometric system//Proceedings of International Conference on Image Processing. Rochester: IEEE: I-57-60[DOI: 10.1109/ICIP.2002.1037958http://dx.doi.org/10.1109/ICIP.2002.1037958]
Jiang X D, Liu M H and Kot A C. 2004. Reference point detection for fingerprint recognition//Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE: 540-543[DOI: 10.1109/ICPR.2004.1334193http://dx.doi.org/10.1109/ICPR.2004.1334193]
Kabir W, Ahmad M O and Swamy M N S. 2019. A multi-biometric system based on feature and score level fusions. IEEE Access, 7:59437-59450[DOI:10.1109/ACCESS.2019.2914992]
Karu K and Jain A K. 1996. Fingerprint classification. Pattern Recognition, 29(3):389-404[DOI:10.1016/0031-3203(95)00106-9]
Kawagoe M and Tojo A. 1984. Fingerprint pattern classification. Pattern Recognition, 17(3):295-303[DOI:10.1016/0031-3203(84)90079-7]
Kuncheva L I, Bezdek J C and Duin R P W. 2001. Decision templates for multiple classifier fusion:an experimental comparison. Pattern Recognition, 34(2):299-314[DOI:10.1016/s0031-3203(99)00223-x]
Li J, Yau W Y and Wang H. 2006. Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recognition, 39(1):102-114[DOI:10.1016/j.patcog.2005.08.010]
Li W. 2006. Research on Pixel-Level Image Fusion Method and Its Application. Guangzhou: South China University of Technology
李伟. 2006.像素级图像融合方法及应用研究.广州: 华南理工大学
Li X Y, Liu T G and Yuan C H. 2013. Research on multi-biometric fusion recognition theory of matching layer[EB/OL]. 2013-01-16[2019-09-20].http://www.paper.edu.cn/releasepaper/content/201301-734http://www.paper.edu.cn/releasepaper/content/201301-734
李秀艳, 刘铁根, 袁臣虎. 2013.匹配层多生物特征融合识别理论研究[EB/OL]. 2013-01-16[2019-09-20].http://www.paper.edu.cn/releasepaper/content/201301-734http://www.paper.edu.cn/releasepaper/content/201301-734
Li Z F, Park U and Jain A K. 2011. A discriminative model for age invariant face recognition. IEEE Transactions on Information Forensics and Security, 6(3):1028-1037[DOI:10.1109/tifs.2011.2156787]
Libby E W and Maybeck P S. 1996. Sequence comparison techniques for multisensory data fusion and target recognition. IEEE Transactions on Aerospace and Electronic Systems, 32(1):52-65[DOI:10.1109/7.481249]
Liu D H, Lan K M and Shen L S. 2005. Illumination invariant face recognition. Pattern Recognition, 38(10):1705-1716[DOI:10.1016/j.patcog.2005.03.009]
Liu H X. 2010. Comprehensive Analysis and Application of Biological Characteristics. Shanghai: Shanghai Jiao Tong University
刘欢喜. 2010.人体生物特征的综合分析与应用.上海: 上海交通大学
Maio D and Maltoni D. 1997. Direct gray-scale minutiae detection in fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1):27-40[DOI:10.1109/34.566808]
Ni N. 2006. Fusion of Face and Palmprint on Decision Level for Identity Verification. Harbin: Harbin Institute of Technology
倪娜. 2006.人脸与掌纹特征在决策层上的融合.哈尔滨: 哈尔滨工业大学
Nilsson K and Bigun J. 2003. Localization of corresponding points in fingerprints by complex filtering. Pattern Recognition Letters, 24(13):2135-2144[DOI:10.1016/s0167-8655(03)00083-7]
O'Gorman L and Nickerson J V. 1989. An approach to fingerprint filter design. Pattern Recognition, 22(1):29-38[DOI:10.1016/0031-3203(89)90035-6]
Park U, Tong Y Y and Jain A K. 2010. Age-invariant face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):947-954[DOI:10.1109/TPAMI.2010.14]
Ratha N K, Chen S Y and Jain A K. 1995. Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition, 28(11):1657-1672[DOI:10.1016/0031-3203(95)00039-3]
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, MA, US: IEEE: 815-823[DOI: 10.1109/CVPR.2015.7298682http://dx.doi.org/10.1109/CVPR.2015.7298682]
Souza G B, Santos D F S, Pires R G, Marana A N and Papa J P. 2017. Deep Boltzmann machines for robust fingerprint spoofing attack detection//Proceedings of 2017 International Joint Conference on Neural Networks. Anchorage: IEEE: 1863-1870[DOI: 10.1109/IJCNN.2017.7966077http://dx.doi.org/10.1109/IJCNN.2017.7966077]
Sun W W. 2011. Research on Multimodal Identification System Based on Face and Fingerprint. Nanjing: Nanjing University of Science and Technology
孙巍巍. 2011.基于人脸与指纹的多模态身份识别系统研究.南京: 南京理工大学
Sun Y, Wang X G and Tang X O. 2014. Deep learning face representation from predicting 10, 000 classes//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE: 1891-1898[DOI: 10.1109/CVPR.2014.244http://dx.doi.org/10.1109/CVPR.2014.244]
Sutcu Y, Li Q M and Memon N. 2007. Secure Biometric templates from fingerprint-face features//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE: 1-6[DOI: 10.1109/CVPR.2007.383385http://dx.doi.org/10.1109/CVPR.2007.383385]
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, OH, USA: IEEE: 1701-1708[DOI: 10.1109/CVPR.2014.220http://dx.doi.org/10.1109/CVPR.2014.220]
Tan X Y and Triggs B. 2010. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6):1635-1650[DOI:10.1109/TIP.2010.2042645]
Trigueros D S, Meng L and Hartnett M. 2018. Face recognition: from traditional to deep learning methods[EB/OL].[2019-09-20].https://arxiv.org/pdf/1811.00116.pdfhttps://arxiv.org/pdf/1811.00116.pdf
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]
Turk M A and Pentland A P. 1991. Face recognition using eigenfaces//Proceedings of 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Maui: IEEE: 586-591[DOI: 10.1109/CVPR.1991.139758http://dx.doi.org/10.1109/CVPR.1991.139758]
Wang J L. 2003. Fingerprint image preprocessing and its improved algorithm. Dalian: Dalian University of Technology
王家隆. 2003.指纹图像的预处理及其改进算法.大连: 大连理工大学
Wang X G and Tang X O. 2004. A unified framework for subspace face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1222-1228[DOI:10.1109/TPAMI.2004.57]
Wu X P. 2009. The Research of Fingerprint Image Preprocessing and Minutiae Extraction Algorithm. Taiyuan: North University of China
吴夏平. 2009.指纹图像预处理和特征提取算法研究.太原: 中北大学
Xiao J L and Zhang J. 2017. A survey of multimodal biometrics recognition methods for joint faces and fingerprints. Journal of University of Shanghai for Science and Technology, 39(1):51-57
肖建力, 张静. 2017.联合人脸与指纹的多模态生物特征识别方法综述.上海理工大学学报, 39(1):51-57)[DOI:10.13255/j.cnki.jusst.2017.01.009]
Yang X, Pei J H and Yang W H. 2001. Fusion evaluation using Sugeno's integral. Chinese Journal of Computer, 24(8):815-818
杨烜, 裴继红, 杨万海. 2001.基于模糊积分的融合图像评价方法.计算机学报, 24(8):815-818)[DOI:10.3321/j.issn:0254-4164.2001.08.006]
Yang Y X. 2011. Research on Multi-Sensor Information Fusion Using D-S Theory and Fuzzy Set Theory. Lanzhou: Lanzhou University of Technology
杨永旭. 2011.基于D-S证据和模糊集理论的多源信息融合算法研究.兰州: 兰州理工大学
Yi D, Lei Z, Liao S C and Li S Z. 2014. Learning face representation from scratch[EB/OL].[2014-11-28].https://arxiv.org/pdf/1411.7923.pdfhttps://arxiv.org/pdf/1411.7923.pdf
Yin D B, Mukhlas A A, Chik R Z W, Othman A T and Omar S. 2018. A proposed approach for biometric-based authentication using of face and facial expression recognition//Proceedings of the 3rd IEEE International Conference on Communication and Information Systems. Singapore: IEEE: 28-33[DOI: 10.1109/ICOMIS.2018.8644974http://dx.doi.org/10.1109/ICOMIS.2018.8644974]
Zhang Y L, Zhou B, Zhan X S, Qiu X G and Lu T P. 2017. Small-size fingerprint matching based on deep learning. Journal of Computer Applications, 37(11):3212-3218, 3225
张永良, 周冰, 詹小四, 裘晓光, 卢天培. 2017.基于深度学习的小面积指纹匹配方法.计算机应用, 37(11):3212-3218, 3225)[DOI:10.11772/j.issn.1001-9081.2017.11.3212]
Zhou J and Gu J W. 2004. A model-based method for the computation of fingerprints' orientation field. IEEE Transactions on Image Processing, 13(6):821-835[DOI:10.1109/TIP.2003.822608]
Zhu E, Yin J P, Zhang G M and Hu C F. 2006. Automatic Fingerprint Recognition Technology. Changsha:National Defense Science and Technology University Press
祝恩, 殷建平, 张国敏, 胡春风. 2006.自动指纹识别技术.长沙:国防科技大学出版社
相关作者
相关机构
京公网安备11010802024621