Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network

Abstract

Facial expression recognition technology was extensively used. This paper develops a multi-branch attention convolutional neural network based on a multiple-branch structure to recognize facial expressions. First, features are extracted from facial images in a multi-branch architecture, and features from three branches are fused. Second, to address the issue of insufficient feature extraction and poor recognition performance, the Convolutional Block Attention Module is added as attention module. Third, our model reduces parameters and computation loads by using depth-wise separable convolutions. The experiments on the FER2013, FERPLUS, and CK+ datasets show that the recognition rates of the proposed model are 69.49%, 84.633%, and 99.39%, respectively. The proposed method has a higher efficiency in extracting image features than traditional deep learning counterparts and achieves high accuracy without complicated artificial feature technology.