Kernel based sparse representation for face recognition

In this paper, we extend the idea of sparse representation into the high dimensional feature space induced by the kernel function, and propose a kernel based test sample sparse representation and classification algorithm (KTSRC) for the first time. The KTSRC is based on the assumption that the test sample can be linearly represented by a part of the training samples in the high dimensional feature space. Although the explicit form of the sample in the feature space is unknown, we can implement the KTSRC by the kernel trick. The experimental results show that the KTSRC achieves promising performance in face recognition, and outperforms the state-of-the-art methods.