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Sparse tensor canonical correlation analysis for micro-expression recognition
Wang, Su-Jing1; Yan, Wen-Jing2; Sun, Tingkai3; Zhao, Guoying4; Fu, Xiaolan5
2016-11-19
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号214期号:0页码:218-232
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
DOI10.1016/j.neucom.2016.05.083
收录类别SCI ; SSCI
语种英语
项目资助者National Natural Science Foundation of China(61379095 ; Beijing Natural Science Foundation(4152055) ; Academy of Finland ; Infotech Oulu ; 61375009 ; 61472138 ; 31500875)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000386741300023
WOS标题词Science & Technology ; Technology
关键词[WOS]LOCAL BINARY PATTERNS ; DISCRIMINANT-ANALYSIS ; FACIAL EXPRESSIONS ; GAIT RECOGNITION ; OPTICAL-FLOW
引用统计
被引频次:7[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
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(0):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(0),218-232.
MLA Wang, Su-Jing,et al."Sparse tensor canonical correlation analysis for micro-expression recognition".NEUROCOMPUTING 214.0(2016):218-232.
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