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Carnegie Mellon Robotics Institute

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Abstract

Despite significant progress in deformable model fitting over the last decade, the problem of efficient and accurate person-independent face ?tting remains a challenging problem. In this work, a reformulation of the generative fitting objective is presented, where only soft correspondences between the model and the image are enforced. This has the dual effect of improving robustness to unseen faces as well as affording fitting time which scales linearly with the model?s complexity. This approach is compared with three state-of-the-art ?tting methods on the problem of person independent face ?tting, where it is shown to closely approach the accuracy of the currently best performing method while affording signi?cant computational savings.

Notes

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

Associated Lab(s) / Group(s): Face Group

Text Reference

Jason M. Saragih, Simon Lucey, and Jeffrey Cohn, "Linear deformable model fitting with soft correspondence constraints," IEEE Conference on Automatic Face and Gesture Recognition (AFGR), September, 2008.

BibTeX Reference

@inproceedings{Saragih_2008_6234,
   author = "Jason M Saragih and Simon Lucey and Jeffrey Cohn",
   title = "Linear deformable model fitting with soft correspondence constraints",
   booktitle = "IEEE Conference on Automatic Face and Gesture Recognition (AFGR)",
   month = "September",
   year = "2008",
}

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