Kernel sparse representation-based classifier ensemble for face recognition

Abstract

Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. The performance of KSRC on face image data is partly dependent on the random projection matrix when using the random projection method and the kernel Gram matrix. This paper develops the kernel sparse representation-based classifier ensemble (KSRCE), which does not require to consider the effect of random projection and kernel Gram matrix on KSRC. Actually, the random projection matrix and the kernel Gram matrix could be used for designing the diversity schemes for KSRCE. In the combination stage, we can combine the labels or the reconstruction errors of a test sample. Experimental results on three face data sets show that KSRCE is very promising.