Regularized Robust Coding for Face Recognition
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TLDR
Wang et al. as discussed by the authors proposed a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients by assuming that the coding residual and the coding coefficient are respectively independent and identically distributed.Abstract:
Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.read more
Citations
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Journal ArticleDOI
Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
TL;DR: This paper presents a two-dimensional image-matrix-based error model, namely, nuclear norm based matrix regression (NMR), for face representation and classification, and develops a fast ADMM algorithm to solve the approximate NMR model.
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Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
TL;DR: Wang et al. as discussed by the authors presented a two-dimensional image matrix based error model, i.e., matrix regression, for face representation and classification, which uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients.
Journal ArticleDOI
Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization
Jielin Jiang,Lei Zhang,Jian Yang +2 more
TL;DR: In WESNR, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously and the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework.
Journal ArticleDOI
Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition
Xudong Jiang,Jian Lai +1 more
TL;DR: A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC and a procedure of supervised low-rank (SLR) dictionary decomposition is proposed to facilitate the proposed SDR framework.
Journal ArticleDOI
Structured Sparse Error Coding for Face Recognition With Occlusion
TL;DR: This work proposes a morphological graph model to describe the morphological structure of the error of the occlusion from two aspects: the error morphology and the error distribution.
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