Recently the sparse representation (or coding) based classifi cation (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fi delity is measured by the � 2-norm or � 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaus- sian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity- constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to out- liers (e.g., occlusions, corruptions, etc.) than SRC. An effi cient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive

RSC\baboon.tif
...\Demo_RSC_AR_disguise.m
...\Demo_RSC_AR_disguise2.m
...\Demo_RSC_FR_noocclusion.asv
...\Demo_RSC_FR_noocclusion.m
...\Demo_RSC_Random_Corruption.asv
...\Demo_RSC_Random_Corruption.m
...\Demo_RSC_Random_Occlusion.asv
...\Demo_RSC_Random_Occlusion.m
...\l1_ls_matlab\@partialDCT\ctranspose.m
...\............\...........\mtimes.m
...\............\...........\partialDCT.m
...\............\find_lambdamax_l1_ls.m
...\............\find_lambdamax_l1_ls_nonneg.m
...\............\l1_ls.m
...\............\l1_ls_nonneg.m
...\............\l1_ls_usrguide.pdf
...\............\operator_example.m
...\............\README.TXT
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...\rand_w_h.mat
...\ReadMe.txt
...\RSC_CVPR11.pdf
...\utilities\Eigenface_f.m
...\.........\Random_Block_Occlu.asv
...\.........\Random_Block_Occlu.m
...\.........\Random_Pixel_Crop.asv
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...\.........\RSC.m
...\.........\Weight_M_update.asv
...\l1_ls_matlab\@partialDCT
...\database
...\l1_ls_matlab
...\utilities
RSC

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