Sparsity analysis versus sparse representation classifier

Sparse representation classifier (SRC) is the state-of-the-art method, and the theory of SRC has interesting links to compressed sensing. This paper proposes a new method named Sparse Regression Analysis (SRA) for object representation and recognition. In SRA, the L1-norm minimization method is combined with regression process to represent the input signal. We show that the discriminative ability of SRC and SRA derives from the fact that the subset which most compactly expresses the input signal is activated in the regression analysis, and SRA is a more direct and powerful way to use compressed sensing for the recognition tasks. To achieve a further improvement, kernelized SRA (KSRA) is developed to make a nonlinear extension of SRA. The experiments are extensively conducted on both palmprint and face recognition, which show that the proposed methods achieve a much better performance than sparse representation classifier, the linear regression classification method, principal component analysis, kernel discriminant analysis, and linear discriminant analysis.