Facial action tracking using an AAM-based CONDENSATION approach
In this paper, we address the problem of tracking a near-frontal view face and its facial features in a video sequence. For this purpose, a particle filtering scheme is proposed, where the distribution of observations is derived from an active appearance model. The dynamics are adaptive in the sense that they are guided by a deterministic search, and the explored area of the state space is adjusted to the quality of the prediction. The number of particles is adapted accordingly, which enables a substantial gain in computing time. In order to account for occlusions, the observation model uses a robust distance measure. Experiments on real video show encouraging results.