Abstract for oa214_CVPR_2005_paper1

In Proc. IEEE Conference on Computer Vision Pattern Recognition, 1:581-588, 2005


O. Arandjelović, G. Shakhnarovich, J. Fisher, R. Cipolla and T. Darrell.


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.

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