Facial action tracking using an AAM-based condensation approach
S. Hamlaoui and Franck Davoine
In: IEEE ICASSP, March 18-23, 2005, Philadelphia, U.S.A..

Abstract

In this paper, we address the problem of tracking a nearfrontal view face and its facial features in a video sequence. To this purpose, a particle filtering scheme is proposed, where the distribution of observations is derived from an active appearance model. As in [8], 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.

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