Rank Minimization across Appearance and Shape for AAM Ensemble Fitting

Xin Cheng, Sridha Sridharan, Jason Saragih, Simon Lucey; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 577-584

Abstract


Active Appearance Models (AAMs) employ a paradigm of inverting a synthesis model of how an object can vary in terms of shape and appearance. As a result, the ability of AAMs to register an unseen object image is intrinsically linked to two factors. First, how well the synthesis model can reconstruct the object image. Second, the degrees of freedom in the model. Fewer degrees of freedom yield a higher likelihood of good fitting performance. In this paper we look at how these seemingly contrasting factors can complement one another for the problem of AAM fitting of an ensemble of images stemming from a constrained set (e.g. an ensemble of face images of the same person).

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[bibtex]
@InProceedings{Cheng_2013_ICCV,
author = {Cheng, Xin and Sridharan, Sridha and Saragih, Jason and Lucey, Simon},
title = {Rank Minimization across Appearance and Shape for AAM Ensemble Fitting},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}