Sparse tensor canonical correlation analysis for micro-expression recognition

doi:10.1016/j.neucom.2016.05.083

	Sparse tensor canonical correlation analysis for micro-expression recognition
	Wang,Su-Jing 1; Yan,Wen-Jing 2; Sun,Tingkai 3; Zhao,Guoying 4; Fu,Xiaolan5
第一作者	Wang, Su-Jing
通讯作者邮箱	wangsujing@psych.ac.cn
心理所单位排序	1
摘要	A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) [1] code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information. (C) 2016 Elsevier B.V. All rights reserved.
关键词	Micro-expression recognition Correlation analysis Sparse representation Tensor subspace
	2016-11-19
语种	英语
DOI	10.1016/j.neucom.2016.05.083
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	214 页码:218-232
期刊论文类型	Article
URL	查看原文
收录类别	SCI ; SSCI
WOS关键词	LOCAL BINARY PATTERNS ; DISCRIMINANT-ANALYSIS ; FACIAL EXPRESSIONS ; GAIT RECOGNITION ; OPTICAL-FLOW
WOS标题词	Science & Technology ; Technology
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000386741300023
WOS分区	Q1
Q分类	Q1
测试或任务	micro-expression recognition;Sparse Tensor Canonical Correlation Analysis (STCCA)
因变量指标	recognition accuracy
资助机构	National Natural Science Foundation of China(61379095 ; Beijing Natural Science Foundation(4152055) ; Academy of Finland ; Infotech Oulu ; 61375009 ; 61472138 ; 31500875)
引用统计	被引频次：39[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/311026/20932
专题	脑与认知科学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China; 2.Wenzhou Univ, Coll Teacher Educ, Wenzhou 325035, Peoples R China; 3.Nanjing Univ Sci & Technol, Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China; 4.Univ Oulu, Dept Comp Sci & Engn, Ctr Machine Vis Res, POB 4500, FI-90014 Oulu, Finland; 5.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
第一作者单位	中国科学院行为科学重点实验室
推荐引用方式 GB/T 7714	Wang,Su-Jing,Yan,Wen-Jing,Sun,Tingkai,et al. Sparse tensor canonical correlation analysis for micro-expression recognition[J]. NEUROCOMPUTING,2016,214:218-232.
APA	Wang,Su-Jing,Yan,Wen-Jing,Sun,Tingkai,Zhao,Guoying,&Fu,Xiaolan.(2016).Sparse tensor canonical correlation analysis for micro-expression recognition.NEUROCOMPUTING,214,218-232.
MLA	Wang,Su-Jing,et al."Sparse tensor canonical correlation analysis for micro-expression recognition".NEUROCOMPUTING 214(2016):218-232.

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