Model-based and image-based methods for facial image synthesis, analysis and recognition

Authors:
Demetri Terzopoulos;Yuencheng Lee;M. Alex O. Vasilescu
Affiliations:
Courant Institute of Mathematical Sciences, New York University, New York, NY and Department of Computer Science, University of Toronto, Toronto, ON, Canada;Department of Computer Science, University of Toronto, Toronto, ON, Canada and Courant Institute of Mathematical Sciences, New York University, New York, NY;Department of Computer Science, University of Toronto, Toronto, ON, Canada and Courant Institute of Mathematical Sciences, New York University, New York, NY
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

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Abstract

We review several model-based and image-based methods that we have developed for analyzing, synthesizing, and recognizing facial images. Our model-based methods include a sophisticated, functional model of the human face/head, which incorporates a biomechanical tissue model with embedded muscle actuators, and techniques for applying it to computer animation and expression estimation in video. Our image-based methods include Tensor-Faces, a nonlinear (multilinear) representation for facial image ensembles that disentangles pose, illumination, and expression effects to improve facial recognition.