Micro-Expression Recognition Using Color Spaces

doi:10.1109/TIP.2015.2496314

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	Micro-Expression Recognition Using Color Spaces
	Wang, Su-Jing; Yan, Wen-Jing; Li, Xiaobai; Zhao, Guoying; Zhou, Chun-Guang; Fu, Xiaolan; Yang, Minghao; Tao, Jianhua
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2015-12-01
卷号	24 期号:12 页码:6034-6047
关键词	Micro-expression Recognition Color Spaces Tensor Analysis Local Binary Patterns Facial Action Coding System
DOI	10.1109/TIP.2015.2496314
文献子类	Article
英文摘要	Micro-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.
WOS关键词	LOCAL BINARY PATTERNS ; FACE RECOGNITION ; FACIAL EXPRESSION ; TEXTURE RECOGNITION ; CLASSIFICATION ; DECEPTION ; MODELS
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000364992700004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40842]
专题	模式识别国家重点实验室_智能交互
推荐引用方式 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.