Description: Recently the sparse representation (or coding) based classification (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 fidelity is measured by the 𝑙 2-norm or 𝑙 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian 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 sparsityconstrained
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 outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model.
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RSC
...\baboon.tif
...\database
...\........\AR_database
...\........\AR_database_Occlusion.mat
...\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
...\............\simple_example.m
...\rand_w_h.mat
...\ReadMe.txt
...\utilities
...\.........\Eigenface_f.m
...\.........\Random_Block_Occlu.asv
...\.........\Random_Block_Occlu.m
...\.........\Random_Pixel_Crop.asv
...\.........\Random_Pixel_Crop.m
...\.........\RSC.m
...\.........\Weight_M_update.asv
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