人脸年龄估计的深度学习方法综述

张珂; 王新胜; 郭玉荣; 苏昱坤; 何颖宣

doi:10.11834/jig.180653

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专辑

人脸年龄估计的深度学习方法综述
Survey of deep learning methods for face age estimation
2019年24卷第8期页码：1215-1230
收稿：2018-12-10，

修回：2019-3-13，

纸质出版：2019-08-16
DOI： 10.11834/jig.180653
稿件说明：

移动端阅览

张珂, 王新胜, 郭玉荣, 苏昱坤, 何颖宣. 人脸年龄估计的深度学习方法综述[J]. 中国图象图形学报, 2019,24(8):1215-1230. DOI： 10.11834/jig.180653.

Ke Zhang, Xinsheng Wang, Yurong Guo, Yukun Su, Yingxuan He. Survey of deep learning methods for face age estimation[J]. Journal of Image and Graphics, 2019, 24(8): 1215-1230. DOI： 10.11834/jig.180653.

摘要

目的

人脸年龄估计技术作为一种新兴的生物特征识别技术，已经成为计算机视觉领域的重要研究方向之一。随着深度学习的飞速发展，基于深度卷积神经网络的人脸年龄估计技术已成为研究热点。

方法

本文以基于深度学习的真实年龄和表象年龄估计方法为研究对象，通过调研文献，分析了基于深度学习的人脸年龄估计方法的基本思想和特点，阐述其研究现状，总结关键技术及其局限性，对比了常见人脸年龄估计方法的性能，展望了未来的发展方向。

结果

尽管基于深度学习的人脸年龄估计研究取得了巨大的进展，但非受限条件下年龄估计的效果仍不能满足实际需求，主要因为当前人脸年龄估计研究仍存在以下困难：1）引入人脸年龄估计的先验知识不足；2）缺少兼顾全局和局部细节的人脸年龄估计特征表达方法；3）现有人脸年龄估计数据集的限制；4）实际应用环境下的多尺度人脸年龄估计问题。

结论

基于深度学习的人脸年龄估计技术已取得显著进展，但是由于实际应用场景复杂，容易导致人脸年龄估计效果不佳。对目前基于深度学习的人脸年龄估计技术进行全面综述，从而为研究者解决存在的问题提供便利。

Abstract

Objective

As an important part of human biometrics

age information has extensive application prospects in the fields of security monitoring

human-computer interaction

and video retrieval. As an emerging biometric recognition technology

age estimation technology based on face image is an important research subject in the fields of computer vision and face analysis. With the fast development of deep learning

the face age estimation method based on deep convolutional neural network has become a research hotspot in these fields.

Method

Real and apparent age estimation methods based on deep learning are reviewed based on extensive research and the latest achievements of relevant literature. The basic ideas and characteristics of various methods are analyzed. The research status

key technologies

and limitations based on various age estimation methods are summarized. The performance of various methods on common age estimation datasets is compared. Finally

existing major research problems are summarized and discussed

and potential future research directions are presented.

Result

Face age estimation can be divided into real and apparent age estimation according to the subjectivity and objectivity of age labeling

and it can be divided into age group estimation and age value estimation according to the accuracy of age labeling. With the deep convolutional neural network (DCNN) becoming a hotspot in the field of computer vision

from 5-conv 3-fc's AlexNet 33 to 16-conv 3-fc's VGG-19 network and from 21-conv 1-fc's GoogleNet to thousands of layers of ResNets

the learning ability and the depth of the network have improved considerably. An increasing number of face age estimation researchers are focusing on face age estimation based on DCNN with powerful feature extraction and learning capabilities. According to different views

face age estimation methods based on deep learning can be roughly divided into three categories:regression model

multi-class classification

and rank model. Regression model uses regression analysis to achieve age estimation by establishing a functional model that characterizes the age variation of faces. Regression-based age estimation methods may be affected by overfitting due to the randomness in the aging process and the fuzzy mapping between the appearance of the face and its actual age. The age of a person can be easily divided into several age groups. Age group estimation under unconstrained conditions has become a current research topic

and the multi-classification model is the main means of achieving age group estimation because the regression-based age estimation model has difficulty achieving convergence. Moreover

age group classification can meet the needs of most practical applications. The age estimation model based on the rank model regards the age label as a data sequence and converts the age estimation problem into a problem in which the age to be estimated is greater or less than a certain age

thereby transforming the age estimation problem into a series of binary classification problems. Other technologies in the field of computer vision are applied in face age estimation. Although various deep learning-based face age estimation methods have achieved considerable progress

the performance of age estimation fails to meet the practical needs of unconstrained age estimation because current face age estimation research continues to face the following difficulties and challenges:1) insufficient prior knowledge introduced to face age estimation methods; 2) lack of face age estimation feature representation that considers global and local details; 3) the limitations of existing face age estimation datasets; and 4) multi-scale face age estimation problems in practical application environments.

Conclusion

Deep learning-based face age estimation methods have achieved considerable progress

but they perform poorly due to the complexity of actual application scenarios. A comprehensive review of the current deep learning-based face age estimation techniques is needed to help researchers solve existing problems. Age estimation techniques based on face images are expected to play an important role in the future with the continued efforts of researchers and the in-depth development of related technologies.

关键词

Keywords

references

Angulu R, Tapamo J R, Adewumi A O. Age estimation via face images:a survey[J]. EURASIP Journal on Image and Video Processing, 2018, 2018:42.[DOI:10.1186/s13640-018-0278-6]

Fu Y, Guo G D, Huang T S. Age synthesis and estimation via faces:a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11):1955-1976.[DOI:10.1109/TPAMI.2010.36]

Bruyer R, Scailquin J C. Person recognition and ageing:the cognitive status of addresses-an empirical question[J]. International Journal of Psychology, 1994, 29(3):351-366.[DOI:10.1080/00207599408246548]

Lanitis A, Draganova C, Christodoulou C. Comparing different classifiers for automatic age estimation[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(1):621-628.[DOI:10.1109/TSMCB.2003.817091]

Guo G D, Fu Y, Dyer C R, et al. Image-based human age estimation by manifold learning and locally adjusted robust regression[J]. IEEE Transactions on Image Processing, 2008, 17(7):1178-1188.[DOI:10.1109/TIP.2008.924280]

Wang X M, Liang L Y, Wang Z L, et al. Age estimation by facial image:a survey[J]. Journal of Image and Graphics, 2012, 17(6):603-618.

王先梅, 梁玲燕, 王志良, 等.人脸图像的年龄估计技术研究[J].中国图象图形学报, 2012, 17(6):603-618. [DOI:10.11834/jig.20120601]

Guo G D, Mu G W, Fu Y, et al. Human age estimation using bio-inspired features[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA: IEEE, 2009: 112-119.[ DOI: 10.1109/CVPR.2009.5206681 http://dx.doi.org/10.1109/CVPR.2009.5206681 ] http://cn.bing.com/academic/profile?id=e3baf0a4859cd1c0672856b2f2892f29&encoded=0&v=paper_preview&mkt=zh-cn .

Lowe D G. Object recognition from local scale-invariant features[C]//Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, 1999: 1150-1157.[ DOI: 10.1109/ICCV.1999.790410 http://dx.doi.org/10.1109/ICCV.1999.790410 ] http://cn.bing.com/academic/profile?id=7bad81d7d639259da074dab67aba54d9&encoded=0&v=paper_preview&mkt=zh-cn .

Ricanek K, Tesafaye T. Morph: a longitudinal image database of normal adult age-progression[C]//Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE, 2006: 341-345.[ DOI: 10.1109/FGR.2006.78 http://dx.doi.org/10.1109/FGR.2006.78 ]

Eidinger E, Enbar R, Hassner T. Age and gender estimation of unfiltered faces[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(12):2170-2179.[DOI:10.1109/TIFS.2014.2359646]

Panis G, Lanitis A, Tsapatsoulis N, et al. Overview of research on facial ageing using the FG-NET ageing database[J]. IET Biometrics, 2016, 5(2):37-46.[DOI:10.1049/iet-bmt.2014.0053]

Chen B C, Chen C S, Hsu W H. Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset[J]. IEEE Transactions on Multimedia, 2015, 17(6):804-815.[DOI:10.1109/TMM.2015.2420374]

Rothe R, Timofte R, Van Gool L V. DEX: Deep expectation of apparent age from a single image[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE, 2015: 252-257.[ DOI: 10.1109/iccvw.2015.41 http://dx.doi.org/10.1109/iccvw.2015.41 ]

Gallagher A C, Chen T. Understanding images of groups of people[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 256-263.[ DOI: 10.1109/CVPR.2009.5206828 http://dx.doi.org/10.1109/CVPR.2009.5206828 ]

Escalera S, Fabian J, Pardo P, et al. ChaLearn looking at people 2015: apparent age and cultural event recognitiondatasets and results[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE, 2015: 243-251.[ DOI: 10.1109/ICCVW.2015.40 http://dx.doi.org/10.1109/ICCVW.2015.40 ]

Escalera S, Torres M T, Martínez B, et al. ChaLearn looking at people and faces of the world: face analysis workshop and challenge 2016[C] //Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas, NV, USA: IEEE, 2016: 706-713.[ DOI: 10.1109/CVPRW.2016.93 http://dx.doi.org/10.1109/CVPRW.2016.93 ]

Agustsson E, Timofte R, Escalera S, et al. Apparent and real age estimation in still images with deep residual regressors on appa-real database[C]//Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington, DC, USA: IEEE, 2017.[ DOI: 10.1109/FG.2017.20 http://dx.doi.org/10.1109/FG.2017.20 ]

Kwon Y H, da Vitoria Lobo N. Age classification from facial images[J]. Computer Vision and Image Understanding, 1999, 74(1):1-21.[DOI:10.1006/cviu.1997.0549]

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

Gao F, Ai H Z. Face age classification on consumer images with Gabor feature and fuzzy LDA method[C]//Proceedings of the 3rd International Conference on Advances in Biometrics. Alghero, Italy: Springer, 2009: 132-141.[ DOI: 10.1007/978-3-642-01793-3_14 http://dx.doi.org/10.1007/978-3-642-01793-3_14 ] http://cn.bing.com/academic/profile?id=cbb94b73febafffdbab4afef04957bbc&encoded=0&v=paper_preview&mkt=zh-cn .

Gunay A, Nabiyev V V. Automatic age classification with LBP[C]//Proceedings of the 23rd International Symposium on Computer and Information Sciences. Istanbul, Turkey: IEEE, 2008: 1-4.[ DOI: 10.1109/ISCIS.2008.4717926 http://dx.doi.org/10.1109/ISCIS.2008.4717926 ]

Yan S C, Liu M, Huang T S. Extracting age information from local spatially flexible patches[C]//Proceedings of 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas, NV, USA: IEEE, 2008: 737-740.[ DOI: 10.1109/ICASSP.2008.4517715 http://dx.doi.org/10.1109/ICASSP.2008.4517715 ]

Guo G D, Mu G W. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA: IEEE, 2011: 657-664.[ DOI: 10.1109/CVPR.2011.5995404 http://dx.doi.org/10.1109/CVPR.2011.5995404 ]

Guo G D, Mu G W. Joint estimation of age, gender and ethnicity: CCA vs. PLS[C]//Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. Shanghai, China: IEEE, 2013: 1-6.[ DOI: 10.1109/FG.2013.6553737 http://dx.doi.org/10.1109/FG.2013.6553737 ]

LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.[DOI:10.1038/nature14539]

Zou W Y, Wang X Y, Sun M, et al. Generic object detection with dense neural patterns and regionlets[EB/OL]. 2014-04-16[2018-11-01] . https://arxiv.org/pdf/1404.4316.pdf https://arxiv.org/pdf/1404.4316.pdf .

Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, NV, USA: Curran Associates Inc., 2012: 1097-1105.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. 2015-04-10[2018-11-01] . https://arxiv.org/pdf/1409.1556.pdf https://arxiv.org/pdf/1409.1556.pdf .

Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 1-9.[ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]

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

Yi D, Lei Z, Li S Z. Age estimation by multi-scale convolutional network[C]//Proceedings of the 12th Asian Conference on Computer Vision. Singapore: Springer, 2015: 144-158.[ DOI: 10.1007/978-3-319-16811-1_10 http://dx.doi.org/10.1007/978-3-319-16811-1_10 ]

Wang X L, Guo R, Kambhamettu C. Deeply-learned feature for age estimation[C]//Proceedings of 2015 IEEE Winter Conference on Applications of Computer Vision. Waikoloa, HI, USA: IEEE, 2015: 534-541.[ DOI: 10.1109/WACV.2015.77 http://dx.doi.org/10.1109/WACV.2015.77 ]

Ranjan R, Zhou S, Chen J C, et al. Unconstrained age estimation with deep convolutional neural networks[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. S antiago, Chile: IEEE, 2015.[ DOI: 10.1109/ICCVW.2015.54 http://dx.doi.org/10.1109/ICCVW.2015.54 ]

Chen J C, Patel V M, Chellappa R. Unconstrained face verification using deep CNN features[C]//Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid, NY, USA, IEEE, 2016: 1-9.[ DOI: 10.1109/WACV.2016.7477557 http://dx.doi.org/10.1109/WACV.2016.7477557 ]

Shen W, Guo Y L, Wang Y, et al. Deep regression forests for age estimation[EB/OL]. 2017-12-19[2018-11-01] . https://arxiv.org/pdf/1712.07195.pdf https://arxiv.org/pdf/1712.07195.pdf .

Kontschieder P, Fiterau M, Criminisi A, et al. Deep neural decision forests[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1467-1475.[ DOI: 10.1109/ICCV.2015.172 http://dx.doi.org/10.1109/ICCV.2015.172 ]

Shen W, Zhao K, Guo Y L, et al. Label distribution learning forests[EB/OL]. 2017-10-16[2018-11-01] . https://arxiv.org/pdf/1702.06086.pdf https://arxiv.org/pdf/1702.06086.pdf .

Kuang Z H, Huang C, Zhang W. Deeply learned rich coding for cross-dataset facial age estimation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE Press, 2015: 338-343.[ DOI: 10.1109/ICCVW.2015.52 http://dx.doi.org/10.1109/ICCVW.2015.52 ]

Rothe R, Timofte R, Van Gool L. Deep expectation of real and apparent age from a single image without facial landmarks[J]. International Journal of Computer Vision, 2018, 126(2-4):144-157.[DOI:10.1007/s11263-016-0940-3]

Yang T Y, Huang Y H, Lin Y Y, et al. SSR-Net: a compact soft stagewise regression network for age estimation[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: Morgan Kaufmann, 2018: 1078-1084.[ DOI: 10.24963/ijcai.2018/150 http://dx.doi.org/10.24963/ijcai.2018/150 ]

Agustsson E, Timofte R, Van Gool L. Anchored regression networks applied to age estimation and super resolution[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 1652-1661.[ DOI: 10.1109/ICCV.2017.182 http://dx.doi.org/10.1109/ICCV.2017.182 ]

Liu X, Li S X, Kan M N, et al. AgeNet: deeply learned regressor and classifier for robust apparent age estimation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE Press, 2015: 258-266.[ DOI: 10.1109/ICCVW.2015.42 http://dx.doi.org/10.1109/ICCVW.2015.42 ]

Zhao Y D, Tian S P. Facial age estimation method based on hybrid model of classification and regression[J]. Journal of Computer Applications, 2017, 37(7):1999-2002, 2026.

赵一丁, 田森平.基于分类与回归混合模型的人脸年龄估计方法[J].计算机应用, 2017, 37(7):1999-2002, 2026. [DOI:10.11772/j.issn.1001-9081.2017.07.1999]

Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition[C]//Proceedings of the British Machine Vision Conference. Swansea, UK: BMVA Press, 2015: 41.1-41.12.[ DOI: 10.5244/C.29.41 http://dx.doi.org/10.5244/C.29.41 ]

Zhang K, Gao C, Guo L R, et al. Age estimation with multilevel residual networks in unconstrained conditions[J]. Journal of Computer-Aided Design&Computer Graphics, 2018, 30(2):346-353.

张珂, 高策, 郭丽茹, 等.非受限条件下多级残差网络人脸图像年龄估计[J].计算机辅助设计与图形学学报, 2018, 30(2):346-353. [DOI:10.3724/SP.J.1089.2018.16286]

Levi G, Hassncer T. Age and gender classification using convolutional neural networks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA, USA: IEEE, 2015: 34-42.[ DOI: 10.1109/CVPRW.2015.7301352 http://dx.doi.org/10.1109/CVPRW.2015.7301352 ]

Zhang K, Gao C, Guo L R, et al. Age group and gender estimation in the wild with deep RoR architecture[J]. IEEE Access, 2017, 5:22492-22503.[DOI:10.1109/ACCESS.2017.2761849]

Zhang K, Sun M, Han T X, et al. Residual networks of residual networks:multilevel residual networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 28(6):1303-1314.[DOI:10.1109/TCSVT.2017.2654543]

Hou L, Yu C P, Samaras D. Squared earth mover's distance-based loss for training deep neural networks[EB/OL]. 2017-04-03[2018-11-01] . https://arxiv.org/abs/1611.05916 https://arxiv.org/abs/1611.05916 .

Hou L, Yu C P, Samaras D. Squared earth mover's distance loss for training deep neural networks on ordered-classes[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach: ACM, 2017.

Yang H F, Lin B Y, Chang K Y, et al. Automatic age estimation from face images via deep ranking[C]//Proceedings of the British Machine Vision Conference. Swansea, UK: BMVA Press, 2015: 55.1-55.11.[ DOI: 10.5244/C.29.55 http://dx.doi.org/10.5244/C.29.55 ]

Niu Z X, Zhou M, Wang L, et al. Ordinal regression with multiple output CNN for age estimation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 4920-4928.[ DOI: 10.1109/CVPR.2016.532 http://dx.doi.org/10.1109/CVPR.2016.532 ]

Chen S X, Zhang C J, Dong M, et al. Using ranking-CNN for age estimation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 742-751.[ DOI: 10.1109/CVPR.2017.86 http://dx.doi.org/10.1109/CVPR.2017.86 ]

Tan Z C, Wan J, Lei Z, et al. Efficient group-n encoding and decoding for facial age estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(11):2610-2623.[DOI:10.1109/TPAMI.2017.2779808]

Pan H Y, Han H, Shan S G, et al. Revised contrastive loss for robust age estimation from face[C]//Proceedings of the 24th International Conference on Pattern Recognition. Beijing, China: IEEE, 2018.[ DOI: 10.1109/ICPR.2018.8545864 http://dx.doi.org/10.1109/ICPR.2018.8545864 ]

Geng X. Label distribution learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7):1734-1748.[DOI:10.1109/TKDE.2016.2545658]

Zhou Y, Xue H, Geng X. Emotion distribution recognition from facial expressions[C]//Proceedings of the 23rd ACM International Conference on Multimedia. Brisbane, Australia: ACM, 2015: 1247-1250.[ DOI: 10.1145/2733373.2806328 http://dx.doi.org/10.1145/2733373.2806328 ]

Geng X, Xia Y. Head pose estimation based on multivariate label distribution[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014.[ DOI: 10.1109/CVPR.2014.237 http://dx.doi.org/10.1109/CVPR.2014.237 ]

Yang X, Gao B B, Xing C, et al. Deep label distribution learning for apparent age estimation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE, 2015: 344-350.[ DOI: 10.1109/ICCVW.2015.53 http://dx.doi.org/10.1109/ICCVW.2015.53 ]

Gao B B, Xing C, Xie C W, et al. Deep label distribution learning with label ambiguity[J]. IEEE Transactions on Image Processing, 2017, 26(6):2825-2838.[DOI:10.1109/TIP.2017.2689998]

Gao B B, Zhou H Y, Wu J X, et al. Age estimation using expectation of label distribution learning[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: IJCAI, 2018: 712-718.[ DOI: 10.24963/ijcai.2018/99 http://dx.doi.org/10.24963/ijcai.2018/99 ]

Antipov G, Baccouche M, Berrani S A, et al. Apparent age estimation from face images combining general and children-specialized deep learning models[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshop. Las Vegas, USA: IEEE Press, 2016: 801-809.[ DOI: 10.1109/CVPRW.2016.105 http://dx.doi.org/10.1109/CVPRW.2016.105 ]

Pan H Y, Han H, Shan S G, et al. Mean-variance loss for deep age estimation from a face[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5285-5294.[ DOI: 10.1109/CVPR.2018.00554 http://dx.doi.org/10.1109/CVPR.2018.00554 ]

Rodríguez P, Cucurull G, Gonfaus J M, et al. Age and gender recognition in the wild with deep attention[J]. Pattern Recognition, 2017, 72:563-571.[DOI:10.1016/j.patcog.2017.06.028]

Zhang K, Liu N, Yuan X F, et al. Fine-grained age group classification in the wild[C]//Proceedings of the 24th International Conference on Pattern Recognition. Beijing, China: IEEE, 2018.[ DOI: 10.1109/ICPR.2018.8545333 http://dx.doi.org/10.1109/ICPR.2018.8545333 ]

Ekmekji A. Convolutional neural networks for age and gender classification[EB/OL].[2016-03-11] . http://59.80.44.47/cs231n.stanford.edu/reports/2016/pdfs/003_Report.pdf http://59.80.44.47/cs231n.stanford.edu/reports/2016/pdfs/003_Report.pdf .

Duan M X, Li K L, Li K Q. An ensemble cnn2elm for age estimation[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(3):758-772.[DOI:10.1109/TIFS.2017.2766583]

Han H, Jain A K, Wang F, et al. Heterogeneous face attribute estimation:a deep multi-task learning approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(11):2597-2609.[DOI:10.1109/TPAMI.2017.2738004]

Dehghan A, Ortiz E G, Shu G, et al. DAGER: deep age, gender and emotion recognition using convolutional neural network[EB/OL]. 2017-03-04[2018-11-01] . https://arxiv.org/pdf/1702.04280.pdf https://arxiv.org/pdf/1702.04280.pdf .

Zaghbani S, Boujneh N, Bouhlel M S. Age estimation using deep learning[J]. Computers&Electrical Engineering, 2018, 68:337-347.[DOI:10.1016/j.compeleceng.2018.04.012]

Jiang H Z, Learned-Miller E. Face detection with the faster R-CNN[C]//Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington: IEEE Press, 2017: 650-657.[ DOI: 10.1109/FG.2017.82 http://dx.doi.org/10.1109/FG.2017.82 ]

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

Ranjan R, Sankaranarayanan S, Castillo C D, et al. An all-in-one convolutional neural network for face analysis[C]//Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington, DC, USA: IEEE, 2017: 17-24.[ DOI: 10.1109/FG.2017.137 http://dx.doi.org/10.1109/FG.2017.137 ]