A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition

doi:10.1109/TAFFC.2015.2485205

	A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition
	Liu,Yong-Jin 1; Zhang,Jin-Kai 1; Yan,Wen-Jing 3; Wang,Su-Jing 2; Zhao,Guoying 4,5; Fu,Xiaolan2
第一作者	Liu, Yong-Jin
通讯作者邮箱	[email protected]
心理所单位排序	2
摘要	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.
关键词	Micro-expression optical flow recognition feature
	2016-10-01
语种	英语
DOI	10.1109/TAFFC.2015.2485205
发表期刊	IEEE Transactions on Affective Computing
ISSN	1949-3045
卷号	7 期号:4 页码:299-310
期刊论文类型	Article
URL	查看原文
收录类别	SCI
WOS关键词	FACIAL EXPRESSIONS ; MODELS
WOS标题词	Science & Technology ; Technology
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000389328800001
WOS分区	Q1
Q分类	Q1
测试或任务	micro-expression recognition; micro-expression spotting
因变量指标	optical flow features; True Positive Rate; recall; precision;F1 score
统计软件	Piotr Dollar's Matlab Toolbox
统计方法	Main Directional Maximal Difference (MDMD) Analysis
资助机构	National 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)
引用统计	被引频次：279[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/20934
专题	脑与认知科学国家重点实验室
作者单位	1.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
推荐引用方式 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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