基于带补偿字典的松弛稀疏表示的小样本人脸识别<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201605007

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Abstract In the undersampled face recognition problem with uncontrolled intra-class variations, the auxiliary dictionary can not work quite well. The training dictionary and the auxiliary dictionary in the sparse representation face recognition methods have different representation abilities for the query image. Thus, different demands on the sparsity constraints of these dictionaries at representation stage are discussed. In this paper, a loose sparse representation based classification with auxiliary dictionaries (LSRCAD) is proposed by using different constraints on two types of dictionary respectively. The experiments confirm the effectiveness and the robustness of LSRCAD. LSRCAD outperforms the original sparse representation face recognition methods with auxiliary dictionaries for undersampled face recognition problems.

Key words： Sparse Representation Auxiliary Dictionary Face Recognition Undersampled Problem

Received: 13 May 2015

About author:: 马晓(通讯作者),男,1990年生,博士研究生,主要研究方向为机器学习、模式识别、子空间理论.E-mail:maxiao2012@pku.edu.cn.
(MA Xiao (Corresponding author), born in 1990, Ph.D. candidate. His research interests include machine learning, pattern recognition and subspace theory.)
庄雯璟,女,1989年生,硕士研究生,主要研究方向为图像处理、模式识别.E-mail:zhuangwj@pku.edu.cn.
(ZHUANG Wenjing, born in 1989, master student. Her research interests include image processing and pattern recognition.)
封举富,男,1967年生,博士,教授,主要研究方向为图像处理、模式识别、机器学习、生物特征识别.E-mail:fjf@cis.pku.edu.cn.
(FENG Jufu, born in 1967, Ph.D., professor. His research interests include image processing, pattern recognition, machine learning and biometrics recognition.)

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Cite this article:

MA Xiao,ZHUANG Wenjing,FENG Jufu. Loose Sparse Representation Based Undersampled Face Recognition with Auxiliary Dictionaries[J]. , 2016, 29(5): 439-446.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201605007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I5/439

[1] 马炎.小样本人脸图像识别研究.硕士学位论文.南京:南京信息工程大学, 2011.
(MA Y. Study of Face Recognition with Small Sample Size. Master Dissertation. Nanjing, China: Nanjing University of Information Science & Technology, 2011.)
[2] DONOHO D L. Compressed Sensing. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306.
[3] LEE K C, HO J, KRIEGMAN D J. Acquiring Linear Subspaces for Face Recognition under Variable Lighting. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(5): 684-698.
[4] NASEEM I, TOGNERI R, BENNAMOUN M. Linear Regression for Face Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2010, 32(11): 2106-2112.
[5] WRIGHT J, YANG A Y, GANESH A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2008, 31(2): 210-227.
[6] 杨荣根,任明武,杨静宇.基于稀疏表示的人脸识别方法.计算机科学, 2010, 37(9): 267-269.
(YANG R G, REN M W, YANG J Y. Sparse Representation Based Face Recognition Algorithm. Computer Science, 2010, 37(9): 267-269.)
[7] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Trans on Signal Processing, 2006, 54(11): 4311-4322.
[8] YANG M, ZHANG L, YANG J, et al. Metaface Learning for Sparse Representation Based Face Recognition // Proc of the 17th IEEE International Conference on Image Processing. Hong Kong, China, 2010: 1601-1604.
[9] YANG M, ZHANG L, FENG X C, et al. Fisher Discrimination Dictionary Learning for Sparse Representation // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 543-550.
[10] YANG J F, ZHANG Y. Alternating Direction Algorithms for l₁-Problems in Compressive Sensing. SIAM Journal on Scientific Computing, 2011, 33(1): 250-278.
[11] MALIOUTOV D M, CETIN M, WILLSKY A S, et al. Homotopy Continuation for Sparse Signal Representation // Proc of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, USA, 2005, V: 733-736.
[12] KOH K, KIM S J, BOYD S. An Interior-Point Method for Large-Scale l₁-Regularized Logistic Regression. Journal of Machine lear-ning Research, 2007, 8: 1519-1555.
[13] LIU Y N, WU F, ZHANG Z H, et al. Sparse Representation Using Nonnegative Curds and Whey // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 3578-3585.
[14] GAO S H, TSANG I W H, CHIA L T, et al. Local Features Are Not Lonely-Laplacian Sparse Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 3555-3561.
[15] BERKES P, WHITE B L, FISER J. No Evidence for Active Sparsification in the Visual Cortex[J/OL]. [2015-04-25]. http://papers.nips.cc/paper/3774-no-evidence-for-active-sparsification-in-the-visual-cortex.pdf.
[16] ZHANG L, YANG M, FENG X C. Sparse Representation or Co-llaborative Representation: Which Helps Face Recognition? // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 471-478.
[17] DENG W H, HU J N, GUO J. Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(9): 1864-1870.
[18] DENG W H, HU J N, GUO J. In Defense of Sparsity Based Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 399-406.
[19] SU Y, SHAN S G, CHEN X L, et al. Adaptive Generic Learning for Face Recognition from a Single Sample per Person // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 2699-2706.
[20] WEI C P, WANG Y C F. Learning Auxiliary Dictionaries for Undersampled Face Recognition // Proc of the IEEE International Conference on Multimedia and Expo. San Jose, USA, 2013. DOI: 10.1109/ICME.2013.6607549.
[21] 李月龙,孟丽,封举富,等.基于光照补偿空间的鲁棒人脸识别.中国科学(信息科学), 2013, 43(11): 1398-1409.
(LI Y L, MENG L, FENG J F, et al. Illumination Compensation Subspace Based Robust Face Recognition. Science China(Information Sciences), 2013, 43(11): 1398-1409.)
[22] CANDS E J, ROMBERG J K, TAO T. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.
[23] GROSS R, MATTHEWS I, COHN J, et al. Multi-PIE. Image and Vision Computing, 2010, 28(5): 807-813.
[24] PHILLIPS P J, WECHSLER H, HUANG J, et al. The FERET dataset and Evaluation Procedure for Face-Recognition Algorithms. Image and Vision Computing, 1998, 16(5): 295-306.