Name
Abstract | ||
|---|---|---|
Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IPTA.2016.7820930 | 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) |
Keywords | Field | DocType |
Automatic pain assessment,facial expression,spatio-temporal features,LBP | Computer vision,Continuous assessment,Pattern recognition,Maximum likelihood detection,Pain assessment,Computer science,Support vector machine,Feature extraction,Facial expression,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
2154-512X | 978-1-4673-8911-2 | 1 |
PageRank | References | Authors |
0.35 | 12 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruijing Yang | 1 | 1 | 0.35 |
| Shujun Tong | 2 | 1 | 0.35 |
| Miguel Bordallo López | 3 | 67 | 7.54 |
| Elhocine Boutellaa | 4 | 58 | 8.13 |
| Jinye Peng | 5 | 284 | 40.93 |
| Xiaoyi Feng | 6 | 229 | 38.15 |
| Abdenour Hadid | 7 | 3305 | 146.00 |