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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
First AuthorSu-Jing Wang
2015
Conference Name13th European Conference on Computer Vision (ECCV)
Correspondent Emailwangsujing@psych.ac.cn
Source Publication13th European Conference on Computer Vision (ECCV)
Volume8925
Issue不详
Pages325-338
Conference DateSEP 06-12, 2014
Conference PlaceZurich, SWITZERLAND
Abstract

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.

KeywordMicro-expression Recognition Sparse Representation Dynamic Features Local Binary Pattern Subtle Motion Extraction
DOI10.1007/978-3-319-16178-5_23
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26519
Collection认知与发展心理学研究室
Affiliation1.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
First Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
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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