Conference Paper

Robust sparse coding for face recognition

Hong Kong Polytech. Univ., Hong Kong, China
DOI: 10.1109/CVPR.2011.5995393 Conference: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Source: IEEE Xplore


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 l2-norm or l1-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 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 outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.

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    • "The section seeks to assess the effectiveness of the proposed GCDL method compare to state of the art in sparse classification. Several SRC families 1 and dictionary learning methods are used for comparison that are developed recently, SRC (Wright et al., 2009), RSC (Yang et al., 2011b), ESRC (Deng et al., 2012), and RADL (Wei and Wang, 2015). Baseline considered methods in the literature can be categorized as methods without external dictionary (SRC and RSC) with external dictionary (such as ESRC and RADL wo ) and with both dictionary learning and classification like (SVDL and RADL w DL). "
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    • "Experimental results show that LPDP is more suitable for recognition tasks than LPP. Sparse representation, as a new branch of the state-of-theart techniques for signal representation, has attracted considerable research interests272829303132333435363738. It attempts to preserve the sparse representation structure of the samples in a lowdimensional embedding subspace. "
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    • "The SRC method classifies the test sample by evaluating which class of training samples has the minimum reconstruction error with the associated coding coefficients. This method is selected because of its high performance reported in former studies [12], in addition to being real-time. The SRC is applied on the detected faces by the well-known Viola-Jones [13] method. "
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