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SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos
Zhang, Zhihao1,2; Chen, Tong1,2,3; Meng, Hongying1,4; Liu, Guangyuan1,2; Fu, Xiaolan3,5
Corresponding AuthorChen, Tong(c_tong@swu.edu.cn)
2018
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume6Pages:71143-71151
AbstractMicro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the micro-expression happens, which is a primary step for micro-expression recognition. Previous work in micro-expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named spotting micro-expression convolutional network was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then, a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve a better performance than the existing state-of-art methods.
KeywordSpotting micro-expression apex frame convolutional neural network deep learning
DOI10.1109/ACCESS.2018.2879485
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG)
Funding ProjectGerman Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169] ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China[61301297] ; German Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169] ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China[61301297] ; National Natural Science Foundation of China[61301297] ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000453304600001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS KeywordRECOGNITION
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27769
Collection认知与发展心理学研究室
Corresponding AuthorChen, Tong
Affiliation1.Southwest Univ, Chongqing Key Lab Nonlinear Circuit & Intelligent, Chongqing 400715, Peoples R China
2.Chongqing Key Lab Artificial Intelligence & Serv, Chongqing 400715, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
4.Brunel Univ London, Dept Elect & Comp Engn, London UB8 3PH, England
5.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Zhihao,Chen, Tong,Meng, Hongying,et al. SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos[J]. IEEE ACCESS,2018,6:71143-71151.
APA Zhang, Zhihao,Chen, Tong,Meng, Hongying,Liu, Guangyuan,&Fu, Xiaolan.(2018).SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos.IEEE ACCESS,6,71143-71151.
MLA Zhang, Zhihao,et al."SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos".IEEE ACCESS 6(2018):71143-71151.
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