Name
Abstract | ||
|---|---|---|
The Active Appearance Models [1] and the derived Active Models (AM) [4] allow to robustly track the face of a single user that was previously learnt, but works poorly with multiple or unknown users. Our research aims at improving the tracking robustness by learning from video databases. In this paper, we study the relation between the face texture and the parameter gradient matrix, and propose a statistical approach to dynamically fit the AM to unknown users by estimating the gradient and update matrices from the face texture. We have implemented this algorithm for real time face tracking and experimentally demonstrate its robustness when tracking multiple or unknown users' faces. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-540-92892-8_9 | MMM |
Keywords | Field | DocType |
single user,active appearance models,face tracking,unknown users,update matrix,real time face tracking,statistical approach,tracking robustness,parameter gradient matrix,face texture,active models,unknown user,dynamical active models,active appearance model,virtual reality,real time | Computer vision,Virtual reality,Pattern recognition,Matrix (mathematics),Computer science,Robustness (computer science),Active appearance model,Artificial intelligence,Facial motion capture | Conference |
Volume | ISSN | Citations |
5371 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dianle Zhou | 1 | 9 | 1.57 |
| Patrick Horain | 2 | 48 | 5.75 |