Survey of Expression Action Unit Recognition Based on Deep Learning

SHAO Zhi-wen; ZHOU Yong; TAN Xin; MA Li-zhuang; LIU Bing; YAO Rui

doi:10.12263/DZXB.20210639

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Survey of Expression Action Unit Recognition Based on Deep Learning

SURVEYS AND REVIEWS | 更新时间：2025-12-08

- Survey of Expression Action Unit Recognition Based on Deep Learning
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 2003-2017(2022)
- 作者机构：
  
  1.中国矿业大学计算机科学与技术学院，江苏徐州 221116
  2.矿山数字化教育部工程研究中心，江苏徐州 221116
  3.上海交通大学计算机科学与工程系，上海 200240
  4.华东师范大学计算机科学与技术学院，上海 200062
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210639
  CLC： TP391.4;
- Received：18 May 2021，
  
  Revised：2021-09-16，
  
  Published：25 August 2022
- 稿件说明：
移动端阅览
邵志文,周勇,谭鑫等.基于深度学习的表情动作单元识别综述[J].电子学报,2022,50(08):2003-2017.

SHAO Zhi-wen,ZHOU Yong,TAN Xin,et al.Survey of Expression Action Unit Recognition Based on Deep Learning[J].ACTA ELECTRONICA SINICA,2022,50(08):2003-2017.
邵志文,周勇,谭鑫等.基于深度学习的表情动作单元识别综述[J].电子学报,2022,50(08):2003-2017. DOI： 10.12263/DZXB.20210639.

SHAO Zhi-wen,ZHOU Yong,TAN Xin,et al.Survey of Expression Action Unit Recognition Based on Deep Learning[J].ACTA ELECTRONICA SINICA,2022,50(08):2003-2017. DOI： 10.12263/DZXB.20210639.

摘要

基于深度学习的表情动作单元识别是计算机视觉与情感计算领域的热点课题.每个动作单元描述了一种人脸局部表情动作，其组合可定量地表示任意表情.当前动作单元识别主要面临标签稀缺、特征难捕捉和标签不均衡3个挑战因素.基于此，本文将已有的研究分为基于迁移学习、基于区域学习和基于关联学习的方法，对各类代表性方法进行评述和总结.最后，本文对不同方法进行了比较和分析，并在此基础上探讨了未来动作单元识别的研究方向.

Abstract

Expression action unit(AU) recognition based on deep learning is a hot topic in the fields of computer vision and affective computing. Each AU describes a facial local expression action

and the combinations of AUs can quantitatively represent any expression. Current AU recognition mainly faces three challenging factors

scarcity of labels

difficulty of feature capture

and imbalance of labels. On this basis

this paper categorizes the existing researches into transfer learning based

region learning based

and relation learning based methods

and comments and summarizes each category of representative methods. Finally

this paper compares and analyzes different methods

and further discusses the future research directions of AU recognition.

关键词

Keywords

references

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