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Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features
Su-Jing Wang1,4; Wen-Jing Yan1,2; Guoying Zhao3; Xiaolan Fu1; Chun-Guang Zhou4
第一作者Su-Jing Wang
2015
会议名称13th European Conference on Computer Vision (ECCV)
通讯作者邮箱wangsujing@psych.ac.cn
会议录名称13th European Conference on Computer Vision (ECCV)
卷号8925
期号不详
页码325-338
会议日期SEP 06-12, 2014
会议地点Zurich, SWITZERLAND
摘要

One of important cues of deception detection is microexpression. It has three characteristics: short duration, low intensity and usually local movements. These characteristics imply that micro-expression is sparse. In this paper, we use the sparse part of Robust PCA (RPCA) to extract the subtle motion information of micro-expression. The local texture features of the information are extracted by Local Spatiotemporal Directional Features (LSTD). In order to extract more effective local features, 16 Regions of Interest (ROIs) are assigned based on the Facial Action Coding System (FACS). The experimental results on two micro-expression databases show the proposed method gain better performance. Moreover, the proposed method may further be used to extract other subtle motion information (such as lip-reading, the human pulse, and micro-gesture etc.) from video.

关键词Micro-expression Recognition Sparse Representation Dynamic Features Local Binary Pattern Subtle Motion Extraction
DOI10.1007/978-3-319-16178-5_23
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文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/26519
专题认知与发展心理学研究室
作者单位1.State Key Lab of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences
2.College of Teacher Education, Wenzhou University
3.Center for Machine Vision Research, University of Oulu, Finland
4.College of Computer Science and Technology, Jilin University
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Su-Jing Wang,Wen-Jing Yan,Guoying Zhao,et al. Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features[C],2015:325-338.
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