Facial Expression Recognition Method Combined with Attention Mechanism

Abstract

Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.