抗年龄干扰的人脸识别

doi:10.3969/j.issn.0372-2112.2018.07.008

电子学报 ›› 2018, Vol. 46 ›› Issue (7): 1593-1600.DOI: 10.3969/j.issn.0372-2112.2018.07.008

抗年龄干扰的人脸识别

吴长虹, 苏剑波, 陈叶飞

上海交通大学自动化系, 系统控制与信息处理教育部重点实验室, 上海 200240

收稿日期:2017-01-04 修回日期:2017-06-22 出版日期:2018-07-25
- 作者简介:
- 吴长虹,女,1992年8月生于江苏.上海交通大学自动化系硕士研究生,研究方向为人脸识别、图像处理.E-mail:wuchanghong@sjtu.edu.cn;苏剑波,男,1969年11月生于江苏.上海交通大学自动化系教授,研究方向为机器视觉、机器学习与人机交互、多传感器信息融合、智能机器人等;陈叶飞,男,1988年生于上海.现为上海交通大学自动化系博士,研究方向为人脸识别、深度学习.
- 基金资助:
- 国家自然科学基金 (No.61521063，No.61533012）

Age Invariant Face Recognition

WU Chang-hong, SU Jian-bo, CHEN Ye-fei

Department of Automation, Shanghai Jiao Tong University, Ministry of Education Key Laboratory of System Control and Information Processing, Shanghai 200240, China

Received:2017-01-04 Revised:2017-06-22 Online:2018-07-25 Published:2018-07-25
- Supported by:
- National Natural Science Foundation of China (No.61521063, No.61533012)

摘要/Abstract

摘要： 本文将人脸图像特征分解为身份特征部分和年龄干扰特征部分，并分别投影到两个独立的子空间.为了提高由字典张成的特征子空间的表征能力和区分能力，字典学习过程中同时引入了人脸图像的重构误差约束项和类别约束项.因此，任意的人脸特征都可以由学习到的身份字典和年龄字典投影到对应的身份子空间和年龄子空间，然后再基于身份子空间进行人脸识别，从而使年龄的干扰得到了有效的抑制.通过在MORPH和FGNET数据库上的实验，证实了基于年龄不变的身份特征子空间学习方法能提升人脸识别性能.

关键词: 人脸识别, 年龄干扰, 字典分解, 子空间学习

Abstract: In this paper,the face image is divided into two separated parts:aging effect feature part and identity related feature part.The identity dictionary and the age dictionary are introduced to encode the two feature parts into two separated feature spaces.To make sure the learned dictionaries are discriminative for different classes,the reconstruction error and label matrices constraints are added in the training.Face features can be encoded into identity and age space with the learned identity and age dictionaries.The identity space can be used for further classification.Extensive experiments are conducted on the MORPH and FGNET dataset,illustrating a great improvement over the state-of-the-arts.

Key words: face recognition, age invariance, dictionary decomposition, space learning

中图分类号:

TP391.4

吴长虹, 苏剑波, 陈叶飞. 抗年龄干扰的人脸识别[J]. 电子学报, 2018, 46(7): 1593-1600.

WU Chang-hong, SU Jian-bo, CHEN Ye-fei . Age Invariant Face Recognition[J]. Acta Electronica Sinica, 2018, 46(7): 1593-1600.

参考文献

[1] 杨利平,李武.光照健壮人脸识别的低秩相对梯度直方图特征[J].电子学报,2016,44(8):1940-1946. YANG L P,LI W.Low-rank relative gradient histogram features for illumination-robust face recognition[J].Acta Electronica Sinica,2016,44(8):1940-1946.(in Chinese)
[2] 平强,庄连生,俞能海.姿态和光照可变条件下的仿射最小线性重构误差人脸识别算法[J].电子学报,2012,40 (10):1965-1970. PING Q,ZHUANG L S,YU N H.Affine minimum linear reconstruction error face recognition under varying pose and illumination[J].Acta Electronica Sinica,2012,40(10):1965-1970.(in Chinese)
[3] 辜小花.基于视觉感知和边缘保持的光照不变人脸识别[J].电子学报,2013,41(8):1500-1505. GU X H.Visual perception and edge preserving illumination invariant face recognition[J].Acta Electronica Sinica,2013,41(8):1500-1505.(in Chinese)
[4] GENG X,ZHOU Z H,SMITHMILES K.Automatic age estimation based on facial aging patterns[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2007,29(12):2234.
[5] PARK U,TONG Y,JAIN A K.Age-invariant face recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(5):947-954.
[6] DU J X,ZHAI C M,YE Y Q.Face aging simulation and recognition based on NMF algorithm with sparseness constraints[J].Neurocomputing,2013,116(10):250-259.
[7] GONG D,LI Z,LIN D,et al.Hidden factor analysis for age invariant face recognition[A].Proceedings of the IEEE International Conference on Computer Vision[C].USA:IEEE Computer Society,2013.2872-2879.
[8] CHEN B C,CHEN C S,HSU W H.Cross-age reference coding for age-invariant face recognition and retrieval[A].Proceedings of the European Conference on Computer Vision[C].Cham:Springer,2014.768-783.
[9] KLARE B,JAIN A K.Face recognition across time lapse:On learning feature subspaces[A].Proceedings of the International Joint Conference on Biometrics[C].USA:IEEE,2011.1-8.
[10] LI Z,Park U,JAIN A K.A discriminative model for age invariant face recognition[J].IEEE Transactions on Information Forensics & Security,2011,6(3):1028-1037.
[11] LING H,SOATTO S,RAMANATHAN N,et al.Face verification across age progression using discriminative methods[J].IEEE Transactions on Image Processing,2006,15(11):3349.
[12] OTTO C,HAN H,JAIN A.How does aging affect facial components?[A].Proceedings of the International Conference on Computer Vision[C].Berlin:Springer-Verlag,2012.189-198.
[13] GONG D,LI Z,TAO D,et al.A maximum entropy feature descriptor for age invariant face recognition[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2015.5289-5297.
[14] LI Z,GONG D,LI X,et al.Aging face recognition:A hierarchical learning model based on local patterns selection[J].IEEE Transactions on Image Processing,2016,25(5):2146.
[15] ZHANG L,ZHU P,HU Q,et al.A linear subspace learning approach via sparse coding[A].Proceedings of the IEEE International Conference on Computer Vision[C].USA:IEEE,2012.755-761.
[16] WRIGHT J,MA Y,MAIRAL J,et al.Sparse representation for computer vision and pattern recognition[J].Proceedings of the IEEE,2010,98(6):1031-1044.
[17] AHARON M,ELAD M,BRUCKSTEIN A.$ rm k $-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[18] LIU H D,YANG M,GAO Y,et al.Bilinear discriminative dictionary learning for face recognition[J].Pattern Recognition,2014,47(5):1835-1845.
[19] SUN Y,LIU Q,TANG J,et al.Learning discriminative dictionary for group sparse representation[J].IEEE Transactions on Image Processing,2014,23(9):3816-3828.
[20] ZHAO Y,SU J B.A structured sparse learning approach for efficient facial feature description[A].Proceedings of the IEEE International Conference on Information and Automation[C].USA:IEEE,2013.870-874.
[21] JIANG X,LAI J.Sparse and dense hybrid representation via dictionary decomposition for face recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(5):1067-1079.
[22] ZHANG L,YANG M,FENG X.Sparse representation or collaborative representation:Which helps face recognition?[A].Proceedings of the International Conference on Computer Vision[C].USA:IEEE Computer Society,2011.471-478.
[23] JIANG Z,LIN Z,DAVIS L S.Label consistent K-SVD:Learning a discriminative dictionary for recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(11):2651-2664.
[24] YANG M,ZHANG L,YANG J,et al.Metaface learning for sparse representation based face recognition[A].Proceedings of the IEEE International Conference on Image Processing[C].USA:IEEE,2013.1601-1604.
[25] WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2009,31(2):210.
[26] FG-NET Aging Database[OL].Available:http://www.fgnet.rsunit.com/.2017-06-22.
[27] JR K R,TESAFAYE T.MORPH:A longitudinal image database of normal adult age-progression[A].Proceedings of the International Conference on Automatic Face and Gesture Recognition[C].USA:IEEE,2006.341-345.
[28] LI Z,GONG D,LI X,et al.Learning compact feature descriptor and adaptive matching framework for face recognition[J].IEEE Transactions on Image Processing,2015,24(9):2736-2745.

抗年龄干扰的人脸识别

Age Invariant Face Recognition

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王飞扬, 冀鹏欣, 孙笠, 危倩, 李根, 张忠宝. 一种基于深度学习的动态社交网络用户对齐方法[J]. 电子学报, 2022, 50(8): 1925-1936.
[2]	汪宇玲, 陈立, 黎明, 钟国韵, 何月顺, 常玉祥, 宋伟宁. 基于迹变换和旋转增量调制特征的模糊人脸识别[J]. 电子学报, 2021, 49(12): 2437-2448.
[3]	徐先峰, 张丽, 郎彬, 夏振. 引入感知模型的改进孪生卷积神经网络实现人脸识别算法研究[J]. 电子学报, 2020, 48(4): 643-647.
[4]	张瑞, 蒋晨之, 苏剑波. 基于稀疏特征挑选和概率线性判别分析的表情识别研究[J]. 电子学报, 2018, 46(7): 1710-1718.
[5]	李倩玉, 蒋建国, 齐美彬. 基于改进深层网络的人脸识别算法[J]. 电子学报, 2017, 45(3): 619-625.
[6]	杨利平, 李武. 光照健壮人脸识别的低秩相对梯度直方图特征[J]. 电子学报, 2016, 44(8): 1940-1946.
[7]	杨赛, 赵春霞, 刘凡. 多核学习融合局部和全局特征的人脸识别算法[J]. 电子学报, 2016, 44(10): 2344-2350.
[8]	刘军, 景晓军, 孙松林, 谭有恒. 一种基于可变长起主导作用特征(VLDF)的人脸识别算法[J]. 电子学报, 2015, 43(3): 544-549.
[9]	辜小花. 基于视觉感知和边缘保持的光照不变人脸识别[J]. 电子学报, 2013, 41(8): 1500-1505.
[10]	胡正平, 李静. 基于低秩子空间恢复的联合稀疏表示人脸识别算法[J]. 电子学报, 2013, 41(5): 987-991.
[11]	周家锐;;纪震;沈琳琳;朱泽轩;陈思平;. 基于Gabor小波与Memetic算法的人脸识别方法[J]. 电子学报, 2012, 40(4): 642-646.
[12]	杨飞;苏剑波. 人脸显性特征的融合构造方法及识别[J]. 电子学报, 2012, 40(3): 466-471.
[13]	赵Yue, 苏剑波. 一种用于人脸识别的矢量三角形局部特征模式[J]. 电子学报, 2012, 40(11): 2309-2314.
[14]	平强, 庄连生, 俞能海. 姿态和光照可变条件下的仿射最小线性重构误差人脸识别算法[J]. 电子学报, 2012, 40(10): 1965-1970.
[15]	李文辉;姜园媛;王莹;傅博. 一种基于重采样双向2DLDA融合的人脸识别算法[J]. 电子学报, 2011, 39(11): 2526-2533.