Robust Face Recognition by Group Sparse Representation That Uses Samples from List of Subjects
In this paper we consider group sparsity for robust face recognition. We propose a model for inducing group sparsity with no constraints on the definition of the structure of the group, coupled with locality constrained regularization. We formulate the problem as bounded distance regularized L2 norm minimization with group sparsity inducing, non-convex constrains. We apply convex relaxation and a branch and bound strategy to find an approximation to the original problem. The empirical results confirm that with this approach of deploying a very simple non-overlapping group structure we outperform several state-of-the-art sparse coding based image classification methods.
KeywordsFace recognition sparse representation group sparsity
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