[1]MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11(3):279-286.[doi:10.11992/tis.201603026]
Copy

Sparse representation via deep learning features based face recognition method

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 279-286 Column: 学术论文—机器感知与模式识别 Public date: 2016-06-25
Title:
Sparse representation via deep learning features based face recognition method
Author(s):
MA Xiao1; 2;  ZHANG Fandong1; 2;  FENG Jufu1; 2
1. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
2. Key Laboratory of Machine Perception (Ministry of Education) Department of Machine Intelligence, Peking University, Beijing 100871, China
Keywords:
machine learning; biometric recognition; deep learning; feature learning; subspace; under-sampled recognition; sparse representation; face recognition
CLC:
TP391.4
DOI:
10.11992/tis.201603026
Abstract:
Focusing on the problems that the traditional sparse representation based face recognition methods are not quite robust to intra-class variations, a novel Sparse Representation via Deep Learning Features based Classification (SRDLFC) method is proposed in this paper, employing a deep convolutional neural network to extract facial features and a sparse representation based framework to make classification. Experimental results in this paper also verifies the features extracted from deep convolutional network do satisfy the linear subspace assumption. The proposed SRDLFC proves to be quite effective and be robust to intra-class variations especially for under-sampled face recognition problems.
References:
[1] DONOHO D L. Compressed sensing[J]. Information theory, 2006, 52(4): 1289-1306.
[2] LEE K C, HO J, KRIEGMAN D. Acquiring linear subspaces for face recognition under variable lighting[J]. Pattern analysis and machine intelligence, 2005, 27(5): 684-698.
[3] NASEEM I, TOGNERI R, BENNAMOUN M. Linear regression for face recognition[J]. Pattern analysis and machine intelligence, 2010, 32(11): 2106-2112.
[4] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. Pattern analysis and machine intelligence, 2009, 31(2): 210-227.
[5] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. Signal processing, 2006, 54(11): 4311-4322.
[6] YANG M, ZHANG L, YANG J, et al. Metaface learning for sparse representation based face recognition[C]// Proceedings of the IEEE International Conference on Image Processing, Hong Kong, China, 2010, 119(5):1601-1604.
[7] YANG M, ZHANG D, FENG X, et al. Fisher discrimination dictionary learning for sparse representation[C]// Proceedings of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011, 24(4):543-550.
[8] YANG J, ZHANG Y. Alternating direction algorithms for l1-problems in compressive sensing[J]. arXiv: 0912.1185, 2009, (1):250-278.
[9] UJDAT D M, MALIOUTOV D M, ETIN M, et al. Homotopy continuation for sparse signal representation[C]// Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing,Philadelphia, USA, 2005:733-736.
[10] KOH K, KIM S J, BOYD S P. An interior-point method for large-scale l1-regularized logistic regression[J]. Journal of machine learning research, 2007, 8(8): 1519-1555.
[11] LIU Y, WU F, ZHANG Z. Sparse representation using nonnegative curds and whey[C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010, 119(5):3578-3585.
[12] GAO S, TSANG I W, Chia L, et al. Local features are not lonely-laplacian sparse coding for image classification[C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010, 23(3):3555-3561.
[13] FISER J, BERKES P, WHITE B. No evidence for active sparsification in the visual cortex[C]// Advances in Neural Information Processing Systems,Vancouver, B.C., Canada, 2009: 108-116.
[14] ZHANG D, YANG M, FENG X. Sparse representation or collaborative representation: which helps face recognition?[C]// Proceedings of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011, 6669(5):471-478.
[15] DENG W, HU J, GUO J. Extended SRC: undersampled face recognition via intraclass variant dictionary[J]. Pattern analysis and machine intelligence, IEEE transactions on, 2012, 34(9): 1864-1870.
[16] GUO J. In defense of sparsity based face recognition[C]// Proceedings of the IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013, 9(4):399-406.
[17] SU Y, SHAN S, CHEN X, et al. Adaptive generic learning for face recognition from a single sample per person[C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 2699-2706.
[18] WEI C, WANG Y-F. Learning auxiliary dictionaries for undersampled face recognition[C]// Proceedings of the IEEE International Conference on Multimedia and Expo.San Jose, California, USA, 2013, 2013:1-6.
[19] WANG X, TANG X. Unified subspace analysis for face recognition[C]// null. Proceedings of the IEEE International Conference on Computer Vision. Nice, France, 2003:679-686.
[20] LECUN Y L, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition. Proc IEEE[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[21] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems.Lake Tahoe, Nevada, USA, 2012, 25:2012.
[22] TAIGMAN Y, YANG M, RANZATO M A, et al. Deepface: Closing the gap to human-level performance in face verification[C]// Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014: 1701-1708.
[23] SUN Y, WANG X, TANG X. Deeply learned face representations are sparse, selective, and robust[J]. arXiv: 1412.1265, 2014.
[24] SZEGEDY C, LIU W, JIA Y, ET AL. Going deeper with convolutions[J]. arXiv: 1409.4842, 2014.
[25] SCHROFF F, KALENICHENKO D, PHILBIN J. FaceNet: A unified embedding for face recognition and clustering[J]. arXiv preprint arXiv:1503.03832, 2015.
[26] YI D, LEI Z, LIAO S, ET AL. Learning face representation from scratch[J]. arXiv preprint arXiv:1411.7923. 2014.
[27] V. KAZEMI AND J. SULLIVAN. One millisecond face alignment with an ensemble of regression trees[C]// Pro-ceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.Columbus, OH, USA, 2014:1867-1874.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems