Abstract:Aiming at the problem of redundant features when using deep learning to extract facial expression im? age features,an improved Xception facial expression recognition network based on multi-layer perceptron(MLP)is proposed. In this model,the features extracted from the Xception network are input into the multi-layer perceptron for weighting,the main features are extracted,and the redundant features are filtered out so that the recognition accu? racy is improved. First,the image is scaled to 48*48,then the data set is enhanced,and these processed images are fed into the network model proposed in this paper. A comparison of ablation experiments show that:The correct rec? ognition rates of this model on the CK + dataset,JAFFE dataset,and MMI dataset are 98.991%,99.02% and 80.339% respectively. The correct recognition rates of Xception model on the CK + dataset,JAFFE dataset and MMI dataset are 97.4829%,90.476%,and 74.0678%,respectively. The correct recognition rates of the Xception + 2lay model on the CK + dataset,JAFFE dataset and MMI dataset are 98.04% and 74.0678%,84.06%,and 75.593%,re? spectively. By comparing the above ablation experiments,the recognition accuracy of this method is significantly bet? ter than the Xception model and the Xception + 2lay model. Compared with other models,the effectiveness of this model is also verified.