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Abstract: Recently it has been shown that face verification based on a unconstrained parts approach, comprised of local feature extraction and a mixture of Gaussians classifier, is considerably more robust than holistic approaches in terms of translations (which can be caused by automatic face localization) and pose variations. In this report we first investigate whether features based on Local Principal Component Analysis (LPCA) are more discriminative than the often used features based on the 2D... (Update)
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BibTeX entry: (Update)
@misc{ sanderson-study,
author = "Conrad Sanderson and et al.",
title = "A Study of Local Features for Unconstrained Parts Based Face Classification",
url = "citeseer.ist.psu.edu/sanderson06study.html" }
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