Robust Parameterized Component Analysis: Theory and applications to 2D Facial Appearance Models

 (with Michael J. Black,  Computer Science Department. Brown University. USA)



ABSTRACT: Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion.

In particular, PCA has been widely used to model the variation in the appearance of people's faces.We extend previous work on facial modeling for

tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we develop person-specific facial appearance

models (PSFAM),which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in

previous work, this has involved a time consuming and error-prone hand alignment and cropping process. Instead, we introduce parameterized

component analysis to  learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM

given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques

achieving sub-pixel accuracy. We illustrate the use of the 2D PSFAM model with several applications including video-conferencing, realistic avatar

animation and preliminary experiments with driver fatigue detection.










De la Torre, F. and Black, M. J., Robust Parameterized Component Analysis: Theory and applications to 2D facial appearance models.,  to appear: Computer Vision and Image Understanding. Special issue  on Face Recogntion,



De la Torre, F. and Black, M. J., Robust Parameterized Component Analysis: Applications to 2D facial  modeling.  European Conf. on Computer Vision, ECCV2002.



De la Torre, F. Automatic Learning of Appearance Face Models.
Second International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems (RATFG-RTS 2001).



De la Torre, F., Black, M. J., . Dynamic Coupled  Component Analysis.

IEEE Proc. Computer Vision and Pattern Recognition, CVPR'01, Kauai, Hawaii, Vol. II, pp. 643-650, Dec. 2001.  


















Automatic Learning of 2D Facial appearance Models









Facial appearance learning    (MPEG-1.01Mb) 








Virtual Animation









Face to face  (MPEG-1.07Mb








Eye Tracking










Eye tracking from profile to profile (AVI-3.88M)