Traditional face recognition algorithms are easily affected by lighting， expressions， occlusion， and sparse noise. How to effectively extract data features for traditional face recognition algorithms is one of the most important parts. This paper applies the multi-matrix low-rank decomposition to facial feature extraction， which makes full use of the structural similarity of face datasets and explores the low-rank subspace of the facial images collection， then combines the low-rank matrix recovery model to extract the key features of the test sample. Finally， the principal component analysis （PCA） algorithm is used to reduce the dimensionality of data， and the sparse representation is utilized for classification. The results show that the algorithm in this paper has good recognition accuracy on AR， Yale and CMU_PIE face datasets when samples contain salt and pepper noise， which verifies the robustness of the algorithm to salt and pepper noise.