Head and facial action tracking: comparison of two robust approaches

In this work, we address a method that is able to track simultaneously 3D head movements and facial actions like lip and eyebrow movements in a video sequence. In a baseline framework, an adaptive appearance model is estimated online by the knowledge of a monocular video sequence. This method uses a 3D model of the face and a facial adaptive texture model. Then, we consider and compare two improved models in order to increase robustness to occlusions. First, we use robust statistics in order to downweight the hidden regions or outlier pixels. In a second approach, mixture models provides better integration of occlusions. Experiments demonstrate the benefit of the two robust models. The latter are compared under various occlusions.