Emotion Recognition Using Hidden Markov Models from Facial Temperature Sequence

In this paper, an emotion recognition from facial temporal sequence has been proposed. Firstly, the temperature difference histogram features and five statistical features are extracted from the facial temperature difference matrix of each difference frame in the data sequences. Then the discrete Hidden Markov Models are used as the classifier for each feature. In which, a feature selection strategy based on the recognition results in the training set is introduced. Finally, the results of the experiments on the samples of the USTC-NVIE database demonstrate the effectiveness of our method. Besides, the experiment results also demonstrate that the temperature information of the forehead is more useful than that of the other regions in emotion recognition and understanding, which is consistent with some related research results.