• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science ›› 2021, Vol. 43 ›› Issue (04): 704-711.

Previous Articles     Next Articles

An expression recognition model based on deep learning and evidence theory

XU Qi-hua1,2,SUN Bo2   

  1. (1.School of Business,Northwest Normal University,Lanzhou 730070;

    2.School of Artificial Intelligence,Beijing Normal University,Beijing 100875,China)

  • Received:2019-11-04 Revised:2020-04-17 Accepted:2021-04-25 Online:2021-04-25 Published:2021-04-21

Abstract: Facial expression recognition is a further research based on face detection, which is an important research direction in the field of computer vision. The goal of the research is to automatically recognize facial expressions based on micro video and study how to use deep learning technology to assist and promote the development of facial expression recognition technology in a big data environment. A fully automated expression recognition model has been designed to address some of the key technical challenges in the expression intelligence recognition process. The model combines a deep auto-encoding network and a self-attention mechanism to construct a sub-model for automatic extraction of facial expression features, and then the evidence theory is used to fuse the results of multi-feature classification. Experimental results show that the model can significantly improve the accuracy of expression recognition, which has important theoretical significance and research value.


Key words: deep learning, expression recognition, evidence theory, auto-encoding network, self- attention