Face recognition technology was vulnerable to illumination,posture,e xpression and other factors,in order to enhance the robustness of face recognition algorithm,LBP algorithm was used to extract local texture features of face images first,then PCA algorithm was used to project high-dimen sional spatial face images into low dimensional feature space,using LDA algorithm to find the optimal projection ve ctors with face class label information,the dimension of image was further compressed.Finally,compared wi th other classical classifiers such as the K-Nearest Neighbor classifier,Bayes classifier,SVM classifier was used to get great recognition and classification results.ORL and Yale face databases were used to verify the effe ctiveness of the algorithm.In ORL, the recognition rate of the method PCA gets 77% only,PCA combined with LBP gets 83.0%,PCA combined with LDA gets 91.5%,the algorithm SRC based on sparse representation and dictionary learning gets 91.5%,and the proposed method gets 100%,which has been greatly improved compared with other r ecognition algorithms.In Yale,the recognition rate of the method PCA gets 74% only,PCA combined with LB P gets 87.9%,PCA combined with LDA gets 93.3%,the algorithm SRC based on sparse representation and dictio nary learning gets 85.6%,and the proposed method gets 100%.The testing results illustrate that the proposed method has also great recognition performance in this database,which shows this method has feasibility and effectiveness.