On the Dimensionality of Deformable Face Models

Iain Matthews, Jing Xiao, and Simon Baker
tech. report CMU-RI-TR-06-12, Robotics Institute, Carnegie Mellon University, March, 2006


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Abstract
Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lipreading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most existing face models can be classified as either 2D (e.g. Active Appearance Models) or 3D (e.g. Morphable Models.) In this paper we compare 2D and 3D face models along four axes: (1) representational power, (2) construction, (3) real-time fitting, and (4) self occlusion reasoning. For each axis in turn, we outline the differences that result from using a 2D or a 3D face model.

Keywords
Model-based face analysis, 2D Active AppearanceModels, 3D Morphable Models, representational power, modelconstruction, non-rigid structure-from-motion, factorization,real-time fitting, the inverse compositional algorithm, constrainedfitting, occlusion reasoning

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Vision for Safe Driving and People Image Analysis Consortium
Associated Project(s): AAM Fitting Algorithms
Number of pages: 45

Text Reference
Iain Matthews, Jing Xiao, and Simon Baker, "On the Dimensionality of Deformable Face Models," tech. report CMU-RI-TR-06-12, Robotics Institute, Carnegie Mellon University, March, 2006

BibTeX Reference
@techreport{Matthews_2006_5370,
   author = "Iain Matthews and Jing Xiao and Simon Baker",
   title = "On the Dimensionality of Deformable Face Models",
   booktitle = "",
   institution = "Robotics Institute",
   month = "March",
   year = "2006",
   number= "CMU-RI-TR-06-12",
   address= "Pittsburgh, PA",
}