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Face recognition with image sets using manifold density divergence | IEEE Conference Publication | IEEE Xplore

Face recognition with image sets using manifold density divergence


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 fa...Show More

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, 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.
Date of Conference: 20-25 June 2005
Date Added to IEEE Xplore: 25 July 2005
Print ISBN:0-7695-2372-2
Print ISSN: 1063-6919
Conference Location: San Diego, CA, USA

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