The changing process of facial expression contains dynamic texture information, and its accurate extraction is crucial to expression recognition. In this paper, a weighted adaptive symmetric center binary pattern from three orthogonal planes (ASCBP-TOP) is proposed for facial expression recognition. First, facial expression image sequences are partitioned into sub-blocks on different scales to establish a multi-scale space, and ASCBP-TOP is used to extract the dynamic texture features of facial expression image sequences in each scale space and obtain the corresponding feature histograms. Then, these feature histograms are connected in series with different weights to obtain an overall feature histogram for describing the dynamic texture features of facial expression sequences. Finally, support vector machine (SVM) is used to classify and recognize facial expressions. Experiments on Cohn-Kanade and JAFFE databases show that the proposed method is superior to the state-of-the-art methods, and it can extract the dynamic texture information more effectively. In addition, the proposed method is more robust to illumination, expression and pose variations, and it has higher expression recognition rate.
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