Name
Abstract | ||
|---|---|---|
Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TPAMI.2015.2477843 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Head,Magnetic heads,Cameras,Target tracking,Training,Geometry | Computer vision,Graph,Multi-task learning,Pattern recognition,Computer science,3D pose estimation,Pose,Artificial intelligence,Monocular,Classifier (linguistics),Grid | Journal |
Volume | Issue | ISSN |
38 | 6 | 0162-8828 |
Citations | PageRank | References |
52 | 1.03 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Yan | 1 | 691 | 31.13 |
| Elisa Ricci 0002 | 2 | 1393 | 73.75 |
| Ramanathan Subramanian | 3 | 461 | 22.16 |
| Gaowen Liu | 4 | 363 | 11.87 |
| Oswald Lanz | 5 | 462 | 33.34 |
| Nicu Sebe | 6 | 7013 | 403.03 |