文件名称:RSC
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人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-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.
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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下载文件列表
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
...\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