Occlusion and corruption in the training images result in degraded performance of the sparse representation classification (SRC) algorithm in practical applications of face recognition. Aiming at the aforementioned problem, we propose a new face recognition method based on structured low rank representation (SLR) and low rank projection (LRP), called SLR_LRP. Firstly, the original training samples are decomposed via SLR to obtain clean training samples. And a LRP matrix is learned based on the original training samples and the recovered clean samples. Secondly, test samples are projected onto the LRP matrix. Finally, SRC is exploited to classify the corrected test samples. Experiments on the AR and the Extended Yale B face databases demonstrate that the SLR_LRP can effectively deal with the occlusion and pixel corruption in samples.