In order to extract robust facial features and improve the decision-level fusion of multi-regional features, a new expression recognition method based on neighbor smooth binary pattern ( NSBP) feature descriptor and weighted evidence fusion ( WEF) is proposed. First, a NSBP descriptor is proposed to encode the image by determining whether the gray values of the center pixels in the horizontal, vertical and diagonal directions are within the gray value range of two neighborhoods in each gradient. Then the initial basic probability assignments (BPA) of evidences are constructed based on the extracted NSBP texture features of the eyebrows, eyes, and mouth regions. Finally, aiming at the deficiency of Dempster-Shafer (D-S) evidence theory in conflict evidence fusion, a synthetic method of weighted evidence revision is proposed to realize the decision fusion of three regional evidences. Experimental results show that the recognition rate of this method on the Cohn-Kanade ( CK) database is 95. 25%, and the average recognition time is 765 ms, compared with other related methods, the effectiveness of this method is also verified.