The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

In this paper we study the relationship between multi-view Active Appearance Model (AAM) fitting and camera calibration. In the first part of the paper we propose an algorithm to calibrate the relative orientation of a set of N > 1 cameras by fitting an AAM to sets of N images. In essence, we use the human face as a (non-rigid) calibration grid. Our algorithm calibrates a set of 2 x 3 weak-perspective camera projection matrices, projections of the world coordinate system origin into the images, depths of the world coordinate system origin, and focal lengths. We demonstrate that the performance of this algorithm is comparable to a standard algorithm using a calibration grid. In the second part of the paper we show how calibrating the cameras improves the performance of multi-view AAM fitting.

Notes

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

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

Associated Project(s): Facial Expression Analysis, Face Model Building and Fitting, AAM Fitting Algorithms

Number of pages: 8

Text Reference

Seth C. Koterba, Simon Baker, Iain Matthews, Changbo Hu, Jing Xiao, Jeffrey Cohn, and Takeo Kanade, "Multi-View AAM Fitting and Camera Calibration," Proc. International Conference on Computer Vision, October, 2005, pp. 511 - 518.

BibTeX Reference

@inproceedings{Koterba_2005_5106,
   author = "Seth C Koterba and Simon Baker and Iain Matthews and Changbo Hu and Jing Xiao and Jeffrey Cohn and Takeo Kanade",
   title = "Multi-View AAM Fitting and Camera Calibration",
   booktitle = "Proc. International Conference on Computer Vision",
   pages = "511 - 518",
   month = "October",
   year = "2005",
   volume = "1",
}

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