Convexity and Bayesian Constrained Local Models

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Convexity and Bayesian Constrained Local Models
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 inference problem, in which it appears as a particular maximum likelihood solution. The Bayesian viewpoint holds many advantages: for example, the task of feature localization can explicitly build on previous face detection stages, and multiple sets of patch responses can be seamlessly incorporated. A second contribution of the paper is an analytic solution to finding convex approximations to patch response surfaces, which removes CQF’s reliance on a numeric optimizer. Improvements in...
Ulrich Paquet (Imense Ltd)
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Ulrich Paquet (Imense Ltd)
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