In recent years, Sparse Representation based Classifier (SRC) has become a hot issue which has been great successful in face recognition. However, when the SRC reconstructed test samples, it is possible to use the training samples with large difference from the test samples, meanwhile, SRC tends to lose locality information and thus produces unstable classification results. A Self-Paced Learning Weighted Sparse Representation based Classifier (SPL-WSRC) was proposed. It could effectively eliminate the training samples with large difference from the test samples. In addition, locality information between the samples was considered by weighting to improve the classification accuracy and stability. The experimental results on three classical face databases show that the proposed SPL-WSRC algorithm is better than the original SRC algorithm. The effect is more obvious, especially when the training samples are enough.