Name
Abstract | ||
|---|---|---|
We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a Minimum Description Length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/AFGR.2008.4813356 | 2008 8TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2008), VOLS 1 AND 2 |
Keywords | DocType | ISSN |
optical flow,root mean square error,minimum description length,optical imaging,shape,adaptive optics,robustness,face,geometry,root mean square,statistical model,face recognition,statistical analysis,face tracking | Conference | 2326-5396 |
Citations | PageRank | References |
4 | 0.43 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Fang | 1 | 114 | 14.47 |
| Nicholas Costen | 2 | 228 | 28.42 |
| David Cristinacce | 3 | 752 | 32.78 |
| John Darby | 4 | 62 | 6.81 |