Expression recognition method based on evidence theory and local texture

To the question of feature selection and multi-feature fusion in facial expression recognition, a novel fusion model is proposed in this paper based on evidence theory and local feature operator. First, the facial image is divided into several regions with significant recognition features, and the Local Binary Pattern (LBP) textural features of the regions are extracted. Then, the LBP histograms in the local regions are connected into a single histogram list, and Chi-square distance is used as the similarity measure to establish the guidelines for evidence synthesis. Finally, the Dempster-Shafer evidence inference theory (D.S evidence theory) is adopted to accomplish the feature vector fusion of all components and the class judgment of facial expression is performed. Experiment shows that the method is simple and effective, which has a high recognition rate and can improve the performance of the facial expression recognition system to some extent.