Name
Abstract | ||
|---|---|---|
This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models or temporal models for dynamics are not required or used. Tracking facial features simply becomes the independent tracking of a set of points on the face. This allows us to cope with facial configurations not present in the training data. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel-level information to tracked feature position displacements. To improve on this, a novel and robust biased linear predictor is proposed in this paper. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Experimental results also show that this method performs more robustly and accurately than AAMs, without any a priori shape information and with minimal training examples. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459283 | ICCV |
Keywords | DocType | Volume |
robustness,probabilistic logic,active appearance model,active shape model,tracking,training data,pixel,shape,vectors | Conference | 2009 |
Issue | ISSN | ISBN |
1 | 1550-5499 E-ISBN : 978-1-4244-4419-9 | 978-1-4244-4419-9 |
Citations | PageRank | References |
14 | 0.84 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eng-Jon Ong | 1 | 445 | 40.51 |
| Yuxuan Lan | 2 | 119 | 8.21 |
| Barry-John Theobald | 3 | 332 | 25.39 |
| Richard Harvey | 4 | 442 | 33.95 |
| Richard Bowden | 5 | 1840 | 118.50 |