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A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition
Liu, Yong-Jin1; Zhang, Jin-Kai1; Yan, Wen-Jing3; Wang, Su-Jing2; Zhao, Guoying4,5; Fu, Xiaolan2
2016-10-01
Source PublicationIEEE Transactions on Affective Computing
ISSN1949-3045
SubtypeArticle
Volume7Issue:4Pages:299-310
Abstract

Micro-expressions are brief facial movements characterized by short duration, involuntariness and low intensity. Recognition of spontaneous facial micro-expressions is a great challenge. In this paper, we propose a simple yet effective Main Directional Mean Optical-flow (MDMO) feature for micro-expression recognition. We apply a robust optical flow method on micro-expression video clips and partition the facial area into regions of interest (ROIs) based partially on action units. The MDMO is a ROI-based, normalized statistic feature that considers both local statistic motion information and its spatial location. One of the significant characteristics of MDMO is that its feature dimension is small. The length of a MDMO feature vector is 36 x 2 = 72, where 36 is the number of ROIs. Furthermore, to reduce the influence of noise due to head movements, we propose an optical-flow-driven method to align all frames of a micro-expression video clip. Finally, a SVM classifier with the proposed MDMO feature is adopted for micro-expression recognition. Experimental results on three spontaneous micro-expression databases, namely SMIC, CASME and CASME II, show that the MDMO can achieve better performance than two state-of-the-art baseline features, i.e., LBP-TOP and HOOF.

KeywordMicro-expression optical flow recognition feature
DOI10.1109/TAFFC.2015.2485205
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61322206 ; Beijing Natural Science Foundation(4152055) ; Open Projects Program of National Laboratory of Pattern Recognition(201306295) ; TNList Cross-discipline Foundation ; Academy of Finland ; Infotech Oulu ; 61521002 ; 61379095 ; 61375009)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000389328800001
WOS HeadingsScience & Technology ; Technology
WOS KeywordFACIAL EXPRESSIONS ; MODELS
Citation statistics
Cited Times:30[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/20934
Collection脑与认知科学国家重点实验室
Affiliation1.Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
3.Wenzhou Univ, Coll Teacher Educ, Wenzhou 325035, Peoples R China
4.Univ Oulu, Ctr Machine Vis Res, Infotech Oulu, POB 4500, FI-90014 Oulu, Finland
5.Univ Oulu, Dept Elect & Informat Engn, POB 4500, FI-90014 Oulu, Finland
Recommended Citation
GB/T 7714
Liu, Yong-Jin,Zhang, Jin-Kai,Yan, Wen-Jing,et al. A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition[J]. IEEE Transactions on Affective Computing,2016,7(4):299-310.
APA Liu, Yong-Jin,Zhang, Jin-Kai,Yan, Wen-Jing,Wang, Su-Jing,Zhao, Guoying,&Fu, Xiaolan.(2016).A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition.IEEE Transactions on Affective Computing,7(4),299-310.
MLA Liu, Yong-Jin,et al."A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition".IEEE Transactions on Affective Computing 7.4(2016):299-310.
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