Face recognition with image sets using manifold density divergence
Arandjelovic, Ognjen, Shakhnarovich, G, Fisher, J, Cipolla, R and Darrell, T 2005, Face recognition with image sets using manifold density divergence, in CVPR 2005 : Proceedings of the Computer Vision and Pattern Recognition Conference 2005, IEEE, Piscataway, New Jersey, pp. 581-588, doi: 10.1109/CVPR.2005.151.
Computer Vision and Pattern Recognition Conference
Start page
581
End page
588
Total pages
8
Publisher
IEEE
Place of publication
Piscataway, New Jersey
Summary
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, semi-parametric 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.
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