A facial expression recognition algorithm incorporating SVM and explainable residual neural network

To address the problem that traditional convolutional neural networks cannot classify facial expression image features precisely, an interpretable face expression recognition method combining ResNet18 residual network and support vector machines (SVM) is proposed in the paper. The SVM classifier is used to enhance the matching ability of feature vectors and labels under the expression image feature space, to improve the expression recognition effect of the whole model. The class activation mapping and t-distributed stochastic neighbor embedding methods are used to visualize and interpret facial expression recognition’s feature analysis and decision making under the residual neural network. The experimental results and the interpretable visualization analysis show that the algorithm structure can effectively improve the recognition ability of the network. Under the FER2013, JAFFE, and CK+ datasets, it achieved 67.65%, 84.44%, and 96.94% emotional recognition accuracy, respectively, showing a certain generalization ability and superior performance.