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 | |
2015 | |
会议日期 | SEP 06-12, 2014 |
会议地点 | Zurich, SWITZERLAND |
关键词 | Micro-expression Recognition Sparse Representation Dynamic Features Local Binary Pattern Subtle Motion Extraction |
卷号 | 8925 |
期号 | 不详 |
DOI | 10.1007/978-3-319-16178-5_23 |
页码 | 325-338 |
英文摘要 | 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. |
会议录 | 13th European Conference on Computer Vision (ECCV) |
内容类型 | 会议论文 |
源URL | [http://ir.psych.ac.cn/handle/311026/26519] |
专题 | 心理研究所_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 |
推荐引用方式 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]. 见:. Zurich, SWITZERLAND. SEP 06-12, 2014. |
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