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Abstract: Principal component analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion in images. In particular, PCA has been widely used to model the variation in the appearance of people#s faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we consider person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person... (Update)
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...Methods In this section, we explore other possible applications and extensions of RSL to computer vision problems. De la Torre and Black [19] proposed Robust Parameterized Component Analysis, a technique to robustly learn a subspace invariant to geometric transformations...
...we validate its performance on a small Gaussian network. We then show how NBP may be combined with parts based local appearance models [5, 14, 23] to locate and reconstruct occluded facial features. 2. Undirected Graphical Models An undirected graph G is defined by a set of...
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BibTeX entry: (Update)
F. de la Torre and M. J. Black. Robust parameterized component analysis: Theory and applications to 2d facial modeling. In European Conference on Computer Vision, pages 653--669, 2002. http://citeseer.ist.psu.edu/delatorre03robust.html More
@misc{ torre02robust,
author = "F. Torre and M. Black",
title = "Robust parameterized component analysis: Theory and applications to 2d
facial modeling",
text = "F. de la Torre and M. J. Black. Robust parameterized component analysis:
Theory and applications to 2d facial modeling. In European Conference on
Computer Vision, pages 653--669, 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/delatorre03robust.html" }
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