Convexity and Bayesian constrained local models

@article{Paquet2009ConvexityAB,
  title={Convexity and Bayesian constrained local models},
  author={Ulrich Paquet},
  journal={2009 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2009},
  pages={1193-1199}
}
The accurate localization of facial features plays a fundamental role in any face recognition pipeline. Constrained local models (CLM) provide an effective approach to localization by coupling ensembles of local patch detectors for non-rigid object alignment. A recent improvement has been made by using generic convex quadratic fitting (CQF), which elegantly addresses the CLM warp update by enforcing convexity of the patch response surfaces. In this paper, CQF is generalized to a Bayesian… CONTINUE READING
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