Sparse representation based classification (SRC) and kernel methods are applied in many pattern recognition problems.In order to improve the classification accuracy

we propose multiple kernel sparse representation based classification (MKSRC).A fast optimization iteration method to solve sparse coefficients and the associated convergence proof to global optimal solution are given.In order to update the kernel weights of MKSRC

two different updating methods and the associated comparison are given.The experimental results on three face image databases show the superiority of the proposed multiple kernel sparse representation based classification.