Exemplar-based face and facial motion tracking

This paper presents an exemplar-based probabilistic approach for face and facial motion tracking. It is well known that high-level knowledge about facial deformations is essential for robust face and facial motion tracking. Face and facial motion tracking problem is usually formulated as a problem of combining the low-level image information and the high-level knowledge. We propose to select only a few representative facial deformation exemplars as the high-level knowledge. A facial deformation can be approximated by a linear combination of the exemplars up to an error term. We develop a probabilistic mechanism that combines the low-level image information and the information provided by the exemplars in terms of maximum a posteriori. The main advantage of this exemplar-based approach is that it avoids manually labelling a large set of training samples, which is required by many other tracking algorithms to train a high-level knowledge model. Therefore, it can be easily set up for different subjects. Moreover, it provides a unified representation for the facial deformations of different subjects.