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.

[1]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[2]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[3]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[4]  Rama Chellappa,et al.  Adaptive visual tracking and recognition using particle filters , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[5]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[6]  Luc Van Gool,et al.  Object Tracking with an Adaptive Color-Based Particle Filter , 2002, DAGM-Symposium.

[7]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[8]  Alex Pentland,et al.  Parametrized structure from motion for 3D adaptive feedback tracking of faces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[10]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jörgen Ahlberg,et al.  An Active Model for Facial Feature Tracking , 2002, EURASIP J. Adv. Signal Process..

[12]  Larry S. Davis,et al.  A probabilistic framework for rigid and non-rigid appearance based tracking and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[13]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[14]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[15]  Michael Isard,et al.  Learning and Classification of Complex Dynamics , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, ECCV.

[17]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Radek Grzeszczuk,et al.  A data-driven model for monocular face tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[19]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  Stephen J. McKenna,et al.  Head Tracking and Action Recognition in a Smart Meeting Room , 2003 .

[21]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[22]  Marco La Cascia,et al.  Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[25]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[26]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.