Deep Residual Expression Recognition Network to Enhance Inter-class Discrimination

Abstract

Deep facial expression recognition is a challenging task for neural networks to apply in pattern reco-gnition. Compared with face recognition tasks such as identity authentication and feature point recognition, there are a lot of redundant information in facial expression recognition task. To achieve good results, more accurate class-ification is needed. Most researches focus on the generalization and network structure of data and ignore the inter-class relationship of data. In this paper, a deep residual expression recognition network RMRnet (recall matrix distinguished residual net) based on class analysis is proposed. First, the data are fed to the backbone network(Resnet18) to obtain the confusion matrix, after that, the recall matrix is used to analyze the relationship between classes. Then, the branches of network structure are designed based on the inter-class relationship to distinguish the strong-related classes, while the rest classes are designed as the supplementary branch to balance the weak-related class. In the end, by adding those branches to the corresponding position of backbone network, RMRnet is constituted. Compared with baseline method and advanced methods in recent years, experimental results in the popular large-scale database show that the proposed method is more effective than the baseline method and has str-ong competitiveness among a group of advanced methods.

Keywords

• |facial expression recognition|neural network|confusion matrix|inter-class association