Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Extraction of Visual Features for Lipreading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Accurate, Real-Time, Unadorned Lip Tracking
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Appearance-Guided Particle Filtering for Articulated Hand Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust lip contour extraction using separability of multi-dimensional distributions
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Accurate and quasi-automatic lip tracking
IEEE Transactions on Circuits and Systems for Video Technology
A local region based approach to lip tracking
Pattern Recognition
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We present a lip contour tracking algorithm using attractor-guided particle filtering. Usually it is difficult to robustly track the lip contour because the lip contour is highly deformable and the contrast between skin and lip colors is very low. It makes the traditional blind segmentation-based algorithms often fail to have robust and realistic results. But in fact, the lip contour is constrained by the facial muscles, the tracking configuration space can then be represented by a lower dimensional manifold. With this observation, we take some representative lip shapes as the attractors in the lower dimensional manifold. To resolve the low contrast problem, we adopt a color feature selection algorithm to maximize the separability between skin and lip colors. Then we integrate the shape priors and the discriminative feature into the attractor-guided particle filtering framework to track the lip contour. The experimental result shows that we can track the lip contour robustly and efficiently.