In order to further improve the accuracy of facial expression recognition algorithm, this paper proposes a facial expression recognition method that combines the double coding local binary pattern (DCLBP) operator and the histogram of oriented absolute gradient (HOAG) operator. First, the method uses DCLBP operator to extract the local texture features of the face image and the HOAG operator to extract the local shape features of the face image. Then, the two extracted correlation features are fused by the canonical correlation analysis (CCA). Finally, using the support vector machine (SVM) to classify facial expression. Compared with the single feature recognition method and the cascade feature recognition method, the experimental results show that the proposed method achieves better recognition results, and the recognition rate on the CohnKanade (CK) and JAFFE data sets achieves 100% and 9905% respectively, the comparison with other related methods also verified its effectiveness.