• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2021, Vol. 43 ›› Issue (04): 704-711.

• 图形与图像 • 上一篇    下一篇

基于深度学习和证据理论的表情识别模型

徐其华1,2,孙波2   

  1. (1.西北师范大学商学院,甘肃 兰州 730070;2.北京师范大学人工智能学院,北京 100875)

  • 收稿日期:2019-11-04 修回日期:2020-04-17 接受日期:2021-04-25 出版日期:2021-04-25 发布日期:2021-04-21
  • 基金资助:
    国家自然科学基金(71861031);甘肃省高等学校创新能力提升项目(2019B-043)

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

摘要: 表情识别是在人脸检测基础之上的更进一步研究,是计算机视觉领域的一个重要研究方向。将研究的目标定位于基于微视频的表情自动识别,研究在大数据环境下,如何使用深度学习技术来辅助和促进表情识别技术的发展。针对表情智能识别过程中存在的一些关键性技术难题,设计了一个全自动表情识别模型。该模型结合深度自编码网络和自注意力机制,构建了一个人脸表情特征自动提取子模型,然后结合证据理论对多特征分类结果进行有效融合。实验结果表明,该模型能显著提升表情识别的准确度,具有重要的理论意义和研究价值。

关键词: 深度学习, 表情识别, 证据理论, 自编码网络, 自注意力

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