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Carnegie Mellon Robotics Institute

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Abstract

Local experts have been used to great effect for ﬁtting deformable models to images. Typically, the best location in an image for the deformable model’s landmarks are found through a locally exhaustive search using these experts. In order to achieve efficient ﬁtting, these experts should afford an efficient evaluation, which often leads to forms with restricted discriminative capacity. In this work, a framework is proposed in which multiple simple experts can be utilized to increase the capacity of the detections overall. In particular, the use of a mixture of linear classifiers is proposed, the computational complexity of which scales linearly with the number of mixture components. The ﬁtting objective is maximized using the expectation maximization (EM) algorithm, where approximations to the true objective are made in order to facilitate efficient and numerically stable fitting. The efficacy of the proposed approach is evaluated on the task of generic face ﬁtting where performance improvement is observed over two existing methods.

Keywords

Face Fitting

Notes

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

Text Reference

Jason M. Saragih, Simon Lucey, and Jeffrey Cohn, "Deformable Model Fitting with a Mixture of Local Experts," International Conference on Computer Vision (ICCV), August, 2009.

BibTeX Reference

@inproceedings{Saragih_2009_6419,
   author = "Jason M Saragih and Simon Lucey and Jeffrey Cohn",
   title = "Deformable Model Fitting with a Mixture of Local Experts",
   booktitle = "International Conference on Computer Vision (ICCV)",
   month = "August",
   year = "2009",
}

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