Name
Abstract | ||
|---|---|---|
Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TIP.2008.924287 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
belief networks,face recognition,mixture of probabilistic principal component analysis (mppca),specific face,multiview face tracking,online-learned specific face appearance models,whole face class,online em algorithm,generic face model,dynamic bayesian network,collaborative tracking,online learning,specific models,expectation maximization algorithm,specific face model,offline-trained generic face model,mppca model,tracking,robust face tracking,online-learned specific face appearance,principal component analysis,mixture-of-probabilistic principal component analysis,probability,collaborative tracking framework probabilistically,collaborative tracking framework,face detection,face,bayesian methods,coherence,robustness,face tracking,artificial intelligence,movement,em algorithm,algorithms | Computer science,Robustness (computer science),Artificial intelligence,Face detection,Computer vision,Facial recognition system,Pattern recognition,Expectation–maximization algorithm,Biometrics,Facial motion capture,Machine learning,Dynamic Bayesian network,Bayesian probability | Journal |
Volume | Issue | ISSN |
17 | 7 | 1057-7149 |
Citations | PageRank | References |
11 | 0.63 | 21 |
Authors | ||
2 | ||