Tracking facial micro-features using an ensemble of particle filters

In this work we propose a template-based tracker for simultaneously tracking multiple targets such as facial features. Our work falls within the particle fil- tering framework and more specifically it extends the Particle Filtering with Factorized Likelihoods (PFFL) tracking scheme. In this work we introduce the idea of using a distribution per constellation of facial features that. These distri- butions are updated in a two-stage observation process in which appearance and shape likelihoods are evaluated separately. Information from all distributions is used to estimate the head pose, which is needed to apply the shape prior onto the observations. In this work we propose the use of a hierarchical shape prior that is modeled to have at its root the shape described by the centroids of the feature constellations and its leaf nodes the shapes for the feature constellations.Media and Knowledge EngineeringICTElectrical Engineering, Mathematics and Computer Scienc