Paper Info
Title
Micro-expression Recognition Using Dynamic Textures on Tensor Independent Color Space
Abstract
Micro-expression is a brief involuntary facial expression which reveals genuine emotions and helps detect lies. It intrigues psychologists and computer scientists' (especially on computer vision and pattern recognition) interests due to its promising applications in various fields. Recent research reveals that color may provide useful information for expression recognition. In this paper, we propose a novel color space model, Tensor Independent Color Space (TICS), for enhancing the performance of micro-expression recognition. An 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 in the independent color components can get higher accuracy than that in RGB. In addition, we define a set of Regions of Interest (ROIs) based on Facial Action Coding System (FACS) and calculated the dynamic texture histograms for each ROI. The experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performance in TICS is better than that in RGB or gray.
Year
DOI
Venue
2014
10.1109/ICPR.2014.800
ICPR
Keywords
DocType
ISSN
video signal processing,regions of interest,facial action coding system,facs,dynamic textures,emotion recognition,tics,micro-expression color video clip,novel color space model,micro-expression recognition,tensors,dynamic texture histograms,tensor independent color space,image colour analysis
Conference
1051-4651
Citations 
PageRank 
References 
8
0.52
0
Authors
5
Name
Order
Citations
PageRank
Sujing Wang169037.65
Wen-Jing Yan22659.43
Xiaobai Li332521.64
Guoying Zhao43767166.92
Xiaolan Fu578660.72