Abstract: The sparse representation classification (SRC) has been successfully applied in human face recognition，and can achieve the classification of the test sample by using only random projections of sample images. However, due to the light and expression change in practical application, the test sample cannot achieve sparse representation by the linear combination of training samples. This paper proposes a face recognition method based on Metaface and kernel sparse representation, which utilizes the kernel trick to map the original data and Metaface set into kernel space such that the nonlinear similarity of the feature can be solved. Moreover, in the kernel space, the original data are reconstructed by sparse representation to get a concision expression, which integrates the Metaface learning framework to improve the recognition rate globally. The test results on ORL database, Yale database and AR database indicate that the proposed method can achieve a higher recognition ratio than other classical methods such as standard SRC, PCA, and SVM.