Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10265
Title: Regularized robust coding for face recognition.
Authors: Yang, M
Zhang, L 
Yang, J
Zhang, D 
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013, v. 22, no. 5, p. 1753-1766 How to cite?
Journal: IEEE transactions on image processing 
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 (IR(3)C) 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.
URI: http://hdl.handle.net/10397/10265
ISSN: 1057-7149 (print)
1941-0042 (online)
DOI: 10.1109/TIP.2012.2235849
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