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Facial Micro-Expression Recognition Based on Deep Local-Holistic Network

文献类型：期刊论文

作者Li, Jingting3; Wang, Ting2; Wang, Su-Jing1,3
刊名APPLIED SCIENCES-BASEL
出版日期2022-05-01
卷号12期号:9页码:17
关键词hierarchical convolution local-holistic micro-expression recognition robust principal component analysis
DOI10.3390/app12094643
通讯作者Wang, Su-Jing(wangsujing@psych.ac.cn)
英文摘要A micro-expression is a subtle, local and brief facial movement. It can reveal the genuine emotions that a person tries to conceal and is considered an important clue for lie detection. The micro-expression research has attracted much attention due to its promising applications in various fields. However, due to the short duration and low intensity of micro-expression movements, microexpression recognition faces great challenges, and the accuracy still demands improvement. To improve the efficiency of micro-expression feature extraction, inspired by the psychological study of attentional resource allocation for micro-expression cognition, we propose a deep local-holistic network method for micro-expression recognition. Our proposed algorithm consists of two subnetworks. The first is a Hierarchical Convolutional Recurrent Neural Network (HCRNN), which extracts the local and abundant spatio-temporal micro-expression features. The second is a Robust principal-component-analysis-based recurrent neural network (RPRNN), which extracts global and sparse features with micro-expression-specific representations. The extracted effective features are employed for micro-expression recognition through the fusion of sub-networks. We evaluate the proposed method on combined databases consisting of the four most commonly used databases, i.e., CASME, CASME II, CAS(ME)(2) , and SAMM. The experimental results show that our method achieves a reasonably good performance.
收录类别SCI
WOS关键词FACE RECOGNITION
资助项目National Natural Science Foundation of China[U19B2032] ; National Natural Science Foundation of China[62106256] ; National Natural Science Foundation of China[62061136001] ; China Postdoctoral Science Foundation[2020M680738] ; Open Research Fund of the Public Security Behavioral Science Laboratory, People's Public Security University of China[2020SYS12]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000794718300001
出版者MDPI
资助机构National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Open Research Fund of the Public Security Behavioral Science Laboratory, People's Public Security University of China
源URL[http://ir.psych.ac.cn/handle/311026/42718]  
专题心理研究所_中国科学院行为科学重点实验室
通讯作者Wang, Su-Jing
作者单位1.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China
2.Beijing Jiaotong Univ, Dept Comp & Informat Technol, Beijing 100044, Peoples R China
3.Inst Psychol, CAS Key Lab Behav Sci, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jingting,Wang, Ting,Wang, Su-Jing. Facial Micro-Expression Recognition Based on Deep Local-Holistic Network[J]. APPLIED SCIENCES-BASEL,2022,12(9):17.
APA Li, Jingting,Wang, Ting,&Wang, Su-Jing.(2022).Facial Micro-Expression Recognition Based on Deep Local-Holistic Network.APPLIED SCIENCES-BASEL,12(9),17.
MLA Li, Jingting,et al."Facial Micro-Expression Recognition Based on Deep Local-Holistic Network".APPLIED SCIENCES-BASEL 12.9(2022):17.

入库方式： OAI收割

来源：心理研究所

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