Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1842-1849.DOI: 10.3778/j.issn.1673-9418.2011042

• Artificial Intelligence • Previous Articles     Next Articles

Deep Residual Expression Recognition Network to Enhance Inter-class Discrimination

HUANG Hao1,2, GE Hongwei1   

  1. 1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence (Jiangnan Uni-versity), Wuxi, Jiangsu 214122, China
    2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2020-11-12 Revised:2021-01-29 Online:2022-08-01 Published:2021-03-28
  • About author:HUANG Hao, born in 1996, M.S. candidate, student member of CCF. His research interests include pattern recognition and machine learning.
    GE Hongwei, born in 1967, Ph.D., professor, Ph.D. supervisor. His research interests include artificial intelligence, pattern recognition, machine learning, image processing and analysis, etc.
  • Supported by:
    the Innovation Program for Graduate of Jiangsu Province(KYLX16_0781);and the Superior Discipline Construction Project of Jiangsu Higher Education Institutions.


黄浩1,2, 葛洪伟1   

  1. 1. 江苏省模式识别与计算智能工程实验室(江南大学),江苏 无锡 214122
    2. 江南大学 人工智能与计算机学院,江苏 无锡 214122
  • 作者简介:黄浩(1996—),男,湖北黄冈人,硕士研究生,CCF学生会员,主要研究方向为模式识别、机器学习。
  • 基金资助:


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.

Key words: facial expression recognition, neural network, confusion matrix, inter-class association



关键词: 表情识别, 神经网络, 混淆矩阵, 类间关联

CLC Number: