Name
Title | ||
|---|---|---|
Micro-Expression Recognition Using Robust Principal Component Analysis And Local Spatiotemporal Directional Features |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16178-5_23 | COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I |
Keywords | Field | DocType |
Micro-expression recognition, Sparse representation, Dynamic features, Local binary pattern, Subtle motion extraction | Computer vision,Facial Action Coding System,Facial expression recognition,Pattern recognition,Deception,Computer science,Sparse approximation,Local binary patterns,Robust principal component analysis,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
8925 | 0302-9743 | 27 |
PageRank | References | Authors |
0.87 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujing Wang | 1 | 690 | 37.65 |
| Wen-Jing Yan | 2 | 265 | 9.43 |
| Guoying Zhao | 3 | 3767 | 166.92 |
| Xiaolan Fu | 4 | 786 | 60.72 |
| Chunguang Zhou | 5 | 543 | 52.37 |