Research on Classroom Expression Recognition Based on Gabor Convolutional and Transformer
This article mainly studies student expression recognition in complex environments.In response to the problem of complex environmental changes that cannot accurately recognize person's expressions,a facial expression recognition model GVT(Gabor-Vision-Transformer)based on Gabor convolution and Transformer is designed.A feature extraction block GVT block was designed by combining Gabor convolution with the idea of Transformer.By using Gabor convolution to extract local facial features rich in texture and edge information,and then using Transformer to extract long-distance information between global data,we can better learn facial key features and significantly improve the classification performance of the model.The accuracy of GVT on the RAF-DB and FER2013Plus datasets is 88.56％and 87.38％,respectively.Comparative experiments and analysis with multiple other models have verified the superiority of this model.
万方数据
维普
