Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features
Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features
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DOI：
10.1007/978-3-319-16178-5_23
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Sujing Wang;Wen-Jing Yan;Guoying Zhao;Xiaolan Fu;Chunguang Zhou
中科院分区：
文献类型：
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作者：
Sujing Wang;Wen-Jing Yan;Guoying Zhao;Xiaolan Fu;Chunguang Zhou
One of important cues of deception detection is micro-expression. 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.
