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Carnegie Mellon Robotics Institute

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Abstract

Active appearance models (AAMs) are generative parametric models commonly used to track faces in video sequences. A limitation of AAMs is they are not robust to occlusion. A recent extension reformulated the search as an iteratively re-weighted least-squares problem. In this paper we focus on the choice of error function for use in a robust AAM search. We evaluate eight error functions using two performance metrics: accuracy of occlusion detection and fitting robustness. We show for any reasonable error function the performance in terms of occlusion detection is the same. However, this does not mean that fitting performance will be the same. We describe experiments for measuring fitting robustness for images containing real occlusion. The best approach assumes the residuals at each pixel are Gaussianaly distributed, then estimates the parameters of the distribution from images that do not contain occlusion. In each iteration of the search, the error image is used to sample these distributions to obtain the pixel weights.

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): AAMs with Occlusion and AAM Fitting Algorithms

Number of pages: 6

Text Reference

Barry-John Theobald, Iain Matthews, and Simon Baker, "Evaluating Error Functions for Robust Active Appearance Models," Proceedings of the International Conference on Automatic Face and Gesture Recognition, April, 2006, pp. 149 - 154.

BibTeX Reference

@inproceedings{Matthews_2006_5301,
   author = "Barry-John Theobald and Iain Matthews and Simon Baker",
   title = "Evaluating Error Functions for Robust Active Appearance Models",
   booktitle = "Proceedings of the International Conference on Automatic Face and Gesture Recognition",
   pages = "149 - 154",
   month = "April",
   year = "2006",
}

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