The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e.~solving a nonlinear optimization problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show how the appearance variation can be ``projected out'' using this algorithm and how the algorithm can be extended to include a ``shape normalizing'' warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.

Keywords

ctive Appearance Models, AAMs, Active Blobs,Morphable Models, fitting, efficiency, Gauss-Newton gradient descent,inverse compositional image alignment.

Notes

Sponsor: US Office of Naval Research

Grant ID: N00014-00-1-0915, N41756-03-C4024

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Vision for Safe Driving, People Image Analysis Consortium, Face Group

Associated Project(s): AAM Fitting Algorithms, Face and Facial Feature Tracking, Face Model Building and Fitting

Text Reference

Iain Matthews and Simon Baker, "Active Appearance Models Revisited," tech. report CMU-RI-TR-03-02, Robotics Institute, Carnegie Mellon University, April, 2003

BibTeX Reference

@techreport{Matthews_2003_4376,
   author = "Iain Matthews and Simon Baker",
   title = "Active Appearance Models Revisited",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "2003",
   number= "CMU-RI-TR-03-02",
   address= "Pittsburgh, PA",
}

The Robotics Institute

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