Name
Abstract | ||
|---|---|---|
Recently, researchers proposed deterministic and statistical appearance-based 3D head tracking methods which can successfully tackle the image variability and drift problems. However, appearance-based methods dedicated to 3D head tracking may suffer from inaccuracies since these methods are not very sensitive to out-of-plane motion variations. On the other hand, the use of dense 3D facial data provided by a stereo rig or a range sensor can provide very accurate 3D head motions/poses. However, this paradigm requires either an accurate facial feature extraction or a computationally expensive registration technique (e.g., the Iterative Closest Point algorithm). In this paper, we improve our appearance-based 3D face tracker by combining an adaptive appearance model with a robust 3D-to-3D registration technique that uses sparse stereo data. The resulting 3D face tracker combines the advantages of both appearance-based trackers and 3D data-based trackers while keeping the CPU time very close to that required by real-time trackers. We provide experiments and performance evaluation which show the feasibility and usefulness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-75274-5_25 | ADVANCES IN COMPUTER GRAPHICS AND COMPUTER VISION |
Keywords | Field | DocType |
3D face tracking,adaptive appearance models,evaluation,stereo,robust 3D registration | Computer vision,BitTorrent tracker,Central processing unit,Pattern recognition,Computer science,Appearance based,Feature extraction,Active appearance model,Head tracking,Artificial intelligence,Facial motion capture,Iterative closest point | Conference |
Volume | ISSN | Citations |
4 | 1865-0929 | 3 |
PageRank | References | Authors |
0.38 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fadi Dornaika | 1 | 809 | 96.43 |
| Angel Domingo Sappa | 2 | 565 | 33.54 |