Face Recognition with Image Sets Using Manifold Density Divergence (2005)
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BibTeX
@MISC{Arandjelovic05facerecognition,
author = {Ognjen Arandjelovic and et al.},
title = {Face Recognition with Image Sets Using Manifold Density Divergence},
year = {2005}
}
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Abstract
In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face recognition using image sets. We propose a flexible, semiparametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average.
