Name
Abstract | ||
|---|---|---|
•A probabilistic framework is proposed to detect spontaneous micro-expression clips.•The geometric deformation captured by ASM model is utilized as features.•The features are robust to subtle head movement and illumination variation.•The Adaboost algorithm is used to estimate the initial probability for each frame.•The random walk algorithm computes the transition probability by deformation similarity.•Extensive experiments are performed on two spontaneous datasets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.cviu.2015.12.006 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
Micro-expression spotting,Random walk,Active shape model,Geometric deformation,Adaboost | Computer vision,Active shape model,AdaBoost,Deformation modeling,Random walk,Computer science,Procrustes analysis,Correlation,Geometric shape,Artificial intelligence,Spotting,Machine learning | Journal |
Volume | Issue | ISSN |
147 | 1 | 1077-3142 |
Citations | PageRank | References |
15 | 0.57 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaoqiang Xia | 1 | 100 | 13.72 |
| Xiaoyi Feng | 2 | 229 | 38.15 |
| Jinye Peng | 3 | 284 | 40.93 |
| xianlin peng | 4 | 17 | 1.31 |
| Guoying Zhao | 5 | 3767 | 166.92 |