Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation

Abstract

Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust Principal Component Analysis(RPCA) is proposed for face recognition.The training sample matrix of each class is decomposed into a 1ow-rank matrix and an error matrix by RPCA algorithm,and the redundant dictionary is constructed by these two matrices.Kernel sparse representation problem is converted to normal sparse representation problem by matrix transformation,and Orthogonal Matching Pursuit(OMP) technology is used to solve sparse representation problem to obtain sparse representation coefficients.The reconstruction error associated with the each class can be calculated by the sparse coefficients to achieve classification of the test sample.Experimental results show that,compared with Sparse Representation-based Classification(SRC),ESRC(Extended SRC) algorithms,the proposed algorithm has a higher recognition rate and it is robust to noise and error disturbance.