A nonlinear discriminative approach to AAM fitting

Jason Saragih; Roland Goecke

doi:10.1109/ICCV.2007.4409106

A nonlinear discriminative approach to AAM fitting

Jason Saragih^*, Roland Goecke

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

132 Citations (Scopus)

Abstract

The Active Appearance Model (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.

Original language	English
DOIs	https://doi.org/10.1109/ICCV.2007.4409106
Publication status	Published - 2007
Event	2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil Duration: 14 Oct 2007 → 21 Oct 2007

Conference

Conference	2007 IEEE 11th International Conference on Computer Vision, ICCV
Country/Territory	Brazil
City	Rio de Janeiro
Period	14/10/07 → 21/10/07

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10.1109/ICCV.2007.4409106

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A nonlinear discriminative approach to AAM fitting

Abstract

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