Facial micro expression classification and recognition method based on hidden Markov and support vector machine

In order to reduce the misidentification rate and time consumption of facial micro expression classification and recognition, a facial micro expression classification and recognition method based on hidden Markov and support vector machine is proposed. Firstly, the micro expression sample images are preprocessed using bilinear interpolation and mean variance normalisation methods. Secondly, Gabor wavelet transform is used to extract local features of micro expression space. Finally, a hidden Markov model is constructed to extract temporal features, and the optimal feature sequence is searched through Viterbi. The feature sequence is then input into a support vector machine to complete classification and recognition. The experimental results show that the false recognition rate of this method is always below 0.2%, the AUC area is close to 1, and the recognition time is always below 15 ms. Therefore, it indicates that the proposed method significantly improves the accuracy and timeliness of micro expression recognition.