Facial expression recognition with convolutional neural network embedded with attention mechanism

Abstract

Facial expression recognition has always been a complex problem in computer vision. In recent years, with the rapid development of deep learning, some methods based on convolutional neural networks have greatly improved the accuracy of expression recognition. To further improve the accuracy of facial expression recognition, this paper proposes a convolutional neural network facial expression recognition model embedded with attention mechanism. The convolutional layer is used to extract the shallow features of facial expressions, the SE attention module is embedded into the network model to realize the extraction of salient regional features of facial expressions. To effectively prevent gradient explosion and gradient disappearance, and speed up the training and convergence speed of the network, a BN layer is added after each convolutional layer. The convolutional layer is used instead of the fully connected layer, and 1×1 convolution is used to achieve classification, which reduces the number of parameters, avoids the overfitting problem caused by a large amount of calculation, and improves the generalization ability of the model. The experimental results show that the facial expression recognition accuracy of the proposed method on the FER2013 and CK+ public datasets has been improved to a certain extent, which verifies the method's effectiveness.