A Nonlinear Discriminative Approach to AAM Fitting

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.

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Jason Saragih, Roland Göcke
• Keywords: AAM Fitting Formulation, AAM Fitting Utilize, Computer Vision, ICCV 2007, Nonlinear Update Model, Parameter Update Model, Popular Fitting Methods