Research on Facial Expression Recognition Based on Voting Model

Abstract

Abstract In order to improve the recognition rate of real-time classification of facial expressions, we proposed a method of facial expression recognition based on voting mechanism. Firstly, different neural network models are constructed to learn facial features. Then, the extracted features are fed into the classifier to obtain the posterior probability of various features. Finally, through the voting mechanism, the optimal decision-making level fusion is achieved to complete the facial expression classification. Experiments show that the average recognition rate of fer2013, CK+ and JAFFE database is 74.58%, 100% and 100% respectively. Compared with other recognition methods, experiental data show that this method has superior performance, improves the recognition rate and robustness of the algorithm, and ensures the universality of the algorithm.