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Micro-Expression Recognition Using Color Spaces
Wang, Su-Jing1,2; Yan, Wen-Jing3; Li, Xiaobai4; Zhao, Guoying4; Zhou, Chun-Guang2,5; Fu, Xiaolan6; Yang, Minghao7; Tao, Jianhua7
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2015-12-01
Volume24Issue:12Pages:6034-6047
SubtypeArticle
AbstractMicro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.
KeywordMicro-expression Recognition Color Spaces Tensor Analysis Local Binary Patterns Facial Action Coding System
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2015.2496314
WOS KeywordLOCAL BINARY PATTERNS ; FACE RECOGNITION ; FACIAL EXPRESSION ; TEXTURE RECOGNITION ; CLASSIFICATION ; DECEPTION ; MODELS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61379095 ; Beijing Natural Science Foundation(4152055) ; Open Projects Program of National Laboratory of Pattern Recognition(201306295) ; Open Project Program of Key Laboratory of Symbolic Computation and Knowledge Engineering through Ministry of Education, Jilin University ; Academy of Finland and Infotech Oulu ; 61332017 ; 61375009 ; 31500875 ; 61472138)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000364992700004
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10523
Collection模式识别国家重点实验室_语音交互
Affiliation1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China
2.Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
3.Wenzhou Univ, Coll Teacher Educ, Wenzhou 325035, Peoples R China
4.Univ Oulu, Dept Comp Sci & Engn, FI-90014 Oulu, Finland
5.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
6.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Su-Jing,Yan, Wen-Jing,Li, Xiaobai,et al. Micro-Expression Recognition Using Color Spaces[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(12):6034-6047.
APA Wang, Su-Jing.,Yan, Wen-Jing.,Li, Xiaobai.,Zhao, Guoying.,Zhou, Chun-Guang.,...&Tao, Jianhua.(2015).Micro-Expression Recognition Using Color Spaces.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(12),6034-6047.
MLA Wang, Su-Jing,et al."Micro-Expression Recognition Using Color Spaces".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.12(2015):6034-6047.
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