Research and Analysis of Facial Expression Recognition Based on Deep Neural Networks

Abstract

Since the traditional feature extraction algorithm cannot extract a large number of effective high-dimensional expression features, and the traditional convolutional network model has a large number of parameters in expression recognition and weak generalization ability, this paper selects the deep convolutional neural network Xception architecture as the basis for improvement. The core operations in the model are residual module and depthwise separable convolution, and ReLU6 activation function is used. The improved model is trained and tested using the public dataset CK+. Through multiple training experiments, the results show that the improved model has achieved a certain level of facial expression recognition performance.