Search

For face recognition, sparse coding based classification methods have demonstrated the inspiring attraction in dealing with the pixel-level based sparse or gaussian noise, and nuclear norm based matrix regression (NMR) methods have shown the robustness in handling with the image-level based structural noise (e.g., occlusions, illuminations). Such regression methods have two limitations: one is that both ignore the label information of training samples and the similarity relationship between the training samples and the testing ones, the other is that nuclear norm as the rank relaxation can make the obtained solution deviate from the original matrix since its over-relaxations to the larger singular values. To overcome both limitations, this paper presents nonconvex relaxation based matrix regression (NRMR) methods: one is called locality-constrained group sparsity regularized nonconvex matrix gamma norm regression model for the s tructural noise (NRMRS), and the other extends it to the locality-constrained group sparsity regularized matrix gamma and minimax concave plus (MCP) regression model for the m ixed noise (NRMRM), i.e., structural noise plus sparse noise. Two variants of inexact augmented Lagrange multiplier (IALM) algorithms including nonconvex IALM (NC-IALM) and majorization–minization IALM (MM-IALM) are devised to solve the proposed models, respectively. Finally, experiments on Extended Yale B, AR and CUHKFS databases can show the superiority of our methods to face recognition with structural noise and mixed noise. [ABSTRACT FROM AUTHOR]

Face recognition (FR) via regression analysis-based classification has been widely studied in the past several years. Most existing regression analysis methods characterize the pixelwise representation error via l1 -norm or l2 -norm, which overlook the 2D structure of the error image. Recently, the nuclear norm-based matrix regression model is proposed to characterize low-rank structure of the error image. However, the nuclear norm cannot accurately describe the low-rank structural noise when the incoherence assumptions on the singular values does not hold, since it overpenalizes several much larger singular values. To address this problem, this paper presents the robust nuclear norm to characterize the structural error image and then extends it to deal with the mixed noise. The majorization–minimization (MM) method is applied to derive a iterative scheme for minimization of the robust nuclear norm optimization problem. Then, an efficiently alternating direction method of multipliers (ADMM) method is used to solve the proposed models. We use weighted nuclear norm as classification criterion to obtain the final recognition results. Experiments on several public face databases demonstrate the effectiveness of our models in handling with variations of structural noise (occlusion, illumination, and so on) and mixed noise. [ABSTRACT FROM PUBLISHER]

Recently, regression analysis has become a popular method for face recognition. Various robust regression methods have been proposed to handle with different recognition tasks. In this paper, we attempt to achieve this goal by the strategy of adding an adaptive noise dictionary (AND) to the training samples. In contrast to the previous methods, the noise dictionary (ND) is adaptive to different kinds of noise and extracted automatically. To get an effective noise dictionary, the Iteratively Reweighted Robust Principal Component Analysis (IRRPCA) is proposed. A corresponding classifier based on linear regression is presented for recognition. As this adaptive noise dictionary can describe the noise distribution of testing samples, it is robust to various kinds of noise and applicable for recognition tasks with occluded or corrupted images. This method is also extended to deal with misaligned images. Experiments are conducted on AR, Yale B, CMU PIE, CMU Multi-Pie, LFW and Pubfig databases to verify the robustness of our method to variations in occlusion, corruption, illumination, misalignment, etc. [ABSTRACT FROM AUTHOR]