A Nonlinear Discriminative Approach to AAM Fitting

Jason Sarigih, Roland Goecke

Research output: A Conference proceeding or a Chapter in BookConference contribution

112 Citations (Scopus)
9 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings of the Eleventh IEEE International Conference on Computer Vision ICCV2007
Place of PublicationAustralia
PublisherIEEE
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventICCV2007 - Rio de Janeiro, Brazil
Duration: 14 Oct 200720 Oct 2007

Conference

ConferenceICCV2007
CountryBrazil
CityRio de Janeiro
Period14/10/0720/10/07

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