Computer Science ›› 2017, Vol. 44 ›› Issue (3): 296-299.doi: 10.11896/j.issn.1002-137X.2017.03.060

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Jointing Gabor Error Dictionary and Low Rank Representation for Face Recognition

SHOU Zhao-yu and YANG Xiao-fan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Focused on the issues that face images have the problems occlusion,disguise,illumination and facial expression changes in face recognition,an improved face recognition method was proposed.According to the characteristics of the Gabor feature for occlusion,disguise,illumination and facial expression changes has stronger robustness,jointing Gabor error dictionary and low rank representation (GDLRR) for face recognition was proposed.Firstly,the Gabor feature of training samples and testing samples are extracted for making up features dictionaries that is to be tested,respectively.And then,a unit matrix is utilized to extract Gabor feature for training a more compact Gabor error dictionary.Finally,lowest-rank representation of feature dictionary of testing samples is sought for classification by jointing Gabor error dictionary and training feature dictionary.Experiments show that the proposed algorithm has better robustness and recognition results against the different problems in the face recognition.

Key words: Occlusion,Low-rank representation,Gabor feature,Error dictionary,Reduce dimension

[1] TURK M,PENTLAND A.Eigenfaces for recognition [J].Cognitive Neuroscience,1991,3(1):71-86.
[2] BELHUMEUR P N,HESPANHA J P,KRIENGMAN D J.Eigenfaces vs.Fisherfaces:Recognition using class specific linear projection[J].IEEE Trans.Pattern Anal.Machine Intell,1997,19(7):711-720.
[3] YANG J,YANG J Y.Why can LDA be performed in PCAtransformed space?[J].Pattern Recognition,2003,36(2):563-566.
[4] WRIGHT J,YANG Y,GANESH A,et al.Robust Face Recognition via Sparse Representation[J].IEEE Trans.Pattern Anal.Mach.Intell.,2009,31(2):210-227.
[5] YANG M,ZHANG L,ZHANG D.Gabor Feature Based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary[J].Pattern Recognition,2013,6(7):1865-1878.
[6] LIU G,Lin Z,YU Y.Robust subspace segmentation by low-rank representation[C]∥ Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010:663-670.
[7] ZHANG J,DAVIS.Learning structured low-rank representa-tions for image classificition[M]∥ Computer Vision and Pattern Recognition.2013:676-683.
[8] DAUBECHIES I,DEFRIES M,DEMOL C.An iterative thres-holding algorithm for linear inverse problems with a sparsity constraint[J].Commun.Pure,2004,57(11):1413-1457.
[9] BECK A,TEBOULLE M.A fast iterative shrinkage-threshol-ding algorithm for linear inverse problems[J].SIAMJ.Imag.,2009,2009(2):183-202.
[10] BERTSEKAS D P.Computer science and applied mathematics,Constrained Optimization and Lagrange Multiplier Methods[M].Academic Press,Boston,1982.
[11] LIU G,LIN Z,YAN S,et al.Robust recovery of subspace structures by low-rank representation[J].Pattern Analysis and Machine Intelligence,2013,35(1):171-184.
[12] LIU C,WECHSLER H.Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J].IEEE Transactions on Image processing,2002,11(4):467-476.
[13] ZHANG L,YANG M,FENG X C.Sparse representation or collaborative representation which helps face recognition?[C]∥Proc.IEEE Int’l Conf.Computer Vision,2011.

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