A Nonlinear Discriminative Approach to AAM Fitting

Authors: Jason Saragih and Roland Göcke

To be resented at the Eleventh IEEE International Conference on Computer Vision ICCV2007, Rio de Janeiro, Brazil, 14-20 October 2007


The Active AppearanceModel (AAM) is a powerful generative method for modeling and registering deformable visual objects. Most methods for AAM fitting utilize a linear parameter update model in an iterative framework. Despite its popularity, the scope of this approach is severely restricted, both in fitting accuracy and capture range, due to the simplicity of the linear update models used. In this paper, we present an new AAM fitting formulation, which utilizes a nonlinear update model. To motivate our approach, we compare its performance against two popular fitting methods on two publicly available face databases, in which this formulation boasts significant performance improvements.

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©2007 IEEE

Bibtex Entry

AUTHOR = {J. Saragih and R. Goecke},
TITLE = {{A Nonlinear Discriminative Approach to AAM Fitting}},
BOOKTITLE = {{Proceedings of the 11th IEEE International Conference on Computer Vision ICCV 2007}},
ADDRESS = {Rio de Janeiro, Brazil},
PAGES = {--},
MONTH = oct,
YEAR = 2007}

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Last modified: Tue Aug 07 11:41:08 AUS Eastern Standard Time 2007