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A Sparse Representation-Based Face Recognition Using Mixed Group Sparsity

Empirical evidence shows that introducing additional structured priors can reduce complexity of coding data, and achieve better performance. To improve the performance of sparse representation-based classification (SRC), the article based on the potential correlations between the elements of dictionary gets a mixed group sparsity which is composed of dynamic group sparsity and fixed-length group sparsity. To solve the structured sparsity efficiently, structured greedy algorithm (structOMP) is redesigned to fit the new structure. The modification includes search space and its neighbor. Finally, three sparse models are compared by experiments of face recognition, and the results show that the mixed group sparsity can improve the face recognition rate of other sparse models by 10% or more in dealing with corrupted data.