In order to overcome the noise sensitivity disadvantages of traditional Local Binary Pattern (LBP), this paper presents using Gauss filters to preprocess face images to remove interference noise. In order to overcome the no non-local feature extraction disadvantages of traditional LBP, this paper presents a new Multiple Weight Local Binary Pattern (MWLBP) operator. MWLBP operator weights sum spatial region histogram value of different sizes square type neighborhood regional LBP. Compared with traditional LBP, this new operator extracts features in a much larger area and can preserve a certain non-local features while extracting local features at the same time. Compared with Gabor feature and other feature extraction methods, this new operator can control the amount of calculation while preserving multiple scale features. Numerical experimental results in ORL and Yale face datasets show that Gauss filter preprocessing can remove interference noise, and improve recognition accuracy rate. MWLBP has smaller computational complexity, less classifier training time, faster operational efficiency and higher recognition accuracy rate than traditional LBP, Gabor feature and other feature extraction methods.