Facial Expression Recognition Based on Improved ResNet50 Network
MAO Yu
GAO Shang
Abstract：Aiming at the problems of low recognition rate in facial expression recognition and more interference information in feature extraction,a network model for facial expression recognition based on improved ResNet50 network is proposed.By embed-ding the coordinate attention mechanism module into the network model,the model can improve the extraction ability of the expres-sion strongly related feature information,reduce the problem of information overload,and improve the robustness and recognition accuracy of the model.The experiment uses the Adam optimizer and improves it,and combines the exponential decay learning rate to further improve the model training effect.The weighted cross-entropy loss function is used to deal with the problem that the accu-racy of model recognition decreases due to the small amount of data in the face dataset and the uneven distribution of categories.Through experimental verification on the CK+dataset and Fer2013 dataset,the accuracy of facial expression recognition of the im-proved network model reaches 98.77%and 73.51%respectively,which has certain advantages over some similar algorithms.
Keywords：facial expression recognitionResNet50 networkcoordinate attention mechanismAdam optimizercross en-tropy loss function
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：5( 3247-3251 )