基于深度学习的表情动作单元识别综述

邵志文; 周勇; 谭鑫; 马利庄; 刘兵; 姚睿

doi:10.12263/DZXB.20210639

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基于深度学习的表情动作单元识别综述

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- 基于深度学习的表情动作单元识别综述
- Survey of Expression Action Unit Recognition Based on Deep Learning
- 电子学报 2022年50卷第8期页码：2003-2017
- 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：
  
  [ "邵志文男，1994年生，安徽马鞍山人. 2020年获得上海交通大学博士学位.现为中国矿业大学计算机科学与技术学院准聘副教授.曾主持国家自然科学基金青年项目、江苏省“双创博士”项目、中央高校基本科研业务费青年项目等.研究方向为计算机视觉和深度学习，涵盖人脸表情识别、人脸表情合成、人脸配准等领域.E-mail: zhiwen_shao@cumt.edu.cn" ]
  [ "周勇男，1974年生，江苏徐州人.现为中国矿业大学计算机科学与技术学院教授、博士生导师.曾主持国家自然科学基金面上项目、国家863计划子课题、江苏省“333人才工程”和“六大人才高峰”项目等.研究方向为数据挖掘、机器学习和人工智能.E-mail: yzhou@cumt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62106268);江苏省自然科学基金(BK20201346);江苏省“六大人才高峰”项目(2015-DZXX-010);中央高校基本科研基金(2021QN1072)
- DOI：10.12263/DZXB.20210639
  中图分类号： TP391.4;
- 收稿：2021-05-18，
  
  修回：2021-09-16，
  
  纸质出版：2022-08-25
- 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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