Head and facial animation tracking using appearance-adaptive models and particle filters
This paper introduces two frameworks for head and facial animation tracking. The first framework introduces a particle-filter tracker capable of tracking the 3D head pose using a statistical facial texture model. The second framework introduces an appearance-adaptive tracker capable of tracking the 3D head pose and the facial animations in real-time. This framework has the merits of both deterministic and stochastic approaches. It consists of an online adaptive observation model of the face texture together with an adaptive transition motion model. The latter is based on a registration technique between the appearance model and the incoming observation. The second framework extends the concept of Online Appearance Models to the case of tracking 3D non-rigid face motion (3D head pose and facial animations). Tracking long video sequences demonstrated the effectiveness of the developed methods. Accurate tracking was obtained even in the presence of perturbing factors such as illumination changes, significant head pose and facial expression variations as well as occlusions.