Performance driven face animation via non-rigid 3d tracking
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Locating Facial Features and Pose Estimation Using a 3D Shape Model
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Adaptive appearance model and condensation algorithm for robust face tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
3D deformable face tracking with a commodity depth camera
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Adding facial actions into 3D model search to analyse behaviour in an unconstrained environment
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Towards automatic annotation of sign language dictionary corpora
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Video-driven state-aware facial animation
Computer Animation and Virtual Worlds
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In this paper, we develop a novel framework for 3D tracking of the non-rigid face deformation from a single camera. The difficulty of the problem lies in the fact that 3D deformation parameter estimation becomes unstable when there are few reliable facial features correspondences. Unfortunately, this often occurs in real tracking scenario when there is significant illumination change, motion blur or large pose variation. In order to extract more information of feature correspondences, the proposed framework integrates three types of features which discriminate face deformation across different views: 1) the semantic features which provide constant correspondences between 3D model points and major facial features; 2) the silhouette features which provide dynamic correspondences between 3D model points and facial silhouette under varying views; 3) the online tracking features that provide redundant correspondences between 3D model points and salient image features. The integration of these complementary features is important for robust estimation of the 3D parameters. In order to estimate the high dimensional 3D deformation parameters, we develop a hierarchical parameter estimation algorithm to robustly estimate both rigid and non-rigid 3D parameters. We show the importance of both features fusion and hierarchical parameter estimation for reliable tracking 3D face deformation. Experiments demonstrate the robustness and accuracy of the proposed algorithm especially in the cases of agile head motion, drastic illumination change, and large pose change up to profile view.