The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Deformable model ﬁtting has been actively pursued in the computer vision community for over a decade. As a result, numerous approaches have been proposed with varying degrees of success. A class of approaches that has shown substantial promise is one that makes independent predictions regarding locations of the model’s landmarks, which are combined by enforcing a prior over their joint motion. A common theme in innovations to this approach is the replacement of the distribution of probable landmark loca- tions, obtained from each local detector, with simpler parametric forms. This simpliﬁcation substitutes the true objective with a smoothed version of itself, reducing sensitivity to local minima and outlying detections. In this work, a principled optimization strategy is proposed where a nonparametric representation of the landmark distributions is maximized within a hierarchy of smoothed estimates. The resulting update equations are reminiscent of mean-shift but with a subspace constraint placed on the shape’s variability. This approach is shown to outperform other existing methods on the task of generic face ﬁtting.

Keywords

Deformable Model, Alignment, Mean-Shift, Registration

Notes

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

Associated Lab(s) / Group(s): Face Group and Component Analysis

Associated Project(s): Facial Expression Analysis and Generic Active Appearance Models

Text Reference

Jason M. Saragih, Simon Lucey, and Jeffrey Cohn, "Subspace Constrained Mean-Shift," tech. report CMU-RI-TR-09-15, Robotics Institute, Carnegie Mellon University, May, 2009

BibTeX Reference

@techreport{Saragih_2009_6369,
   author = "Jason M Saragih and Simon Lucey and Jeffrey Cohn",
   title = "Subspace Constrained Mean-Shift",
   booktitle = "",
   institution = "Robotics Institute",
   month = "May",
   year = "2009",
   number= "CMU-RI-TR-09-15",
   address= "Pittsburgh, PA",
}

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