关键词: 表情识别, 非对称方向性局部二值模式, 多特征融合, 支持向量机（SVM）

Abstract: Aiming at the shortcomings of Directional Local Binary Pattern（DLBP） that can obtain the feature of image texture only at single scale, an expression recognition algorithm of Asymmetric Region-Directional Local Binary Pattern（AR-DLBP） multi-scale multi-directional fusion is proposed. Firstly, the face expression image is subjected to light compensation preprocessing to eliminate the influence of light and noise, and the key region of the face and eyebrows, eye and mouth are divided, and the contribution degree（CM） of the key area is calculated. It  extracts histogram feature information of the XOR-Asymmetric Region-Directional Local Binary Pattern（XOR-AR-DLBP） of face and critical areas, and weights the cascade of the critical region histogram information and the feature of the whole face image information fusion. Finally, it uses SVM classifier for expression classification and identification. The algorithm has the average recognition rate of 95.71% and 97.99% and the average recognition time of 112 ms and 135 ms respectively in the JAFFE library and CK library. The experimental results show that the algorithm can effectively and accurately complete the recognition of facial expression. The algorithm prepares and divides the critical region of expression by weight compensation and expresses the local and global features, which greatly improves the recognition ability of the feature and contrasts with the traditional algorithm. It is also shown that the algorithm, both in the recognition rate and recognition time, the results are the best.

Key words: facial expression recognition, Asymmetric Region-Directional Local Binary Pattern（AR-DLBP）, multi-feature fusion, Support Vector Machine（SVM）