Evaluation of an appearance-based 3D face tracker using dense 3D data
Machine Vision and Applications
IEEE Transactions on Intelligent Transportation Systems
A particle filtering framework for joint video tracking and pose estimation
IEEE Transactions on Image Processing
Real-time modeling of face deformation for 3d head pose estimation
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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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.