Face Recognition with Image Sets Using Manifold Density Divergence

  • Authors:
  • Ognjen Arandjelovic;Gregory Shakhnarovich;John Fisher;Roberto Cipolla;Trevor Darrell

  • Affiliations:
  • University of Cambridge;Massachusetts Institute of Technology;Massachusetts Institute of Technology;University of Cambridge;Massachusetts Institute of Technology

  • Venue:
  • CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
  • Year:
  • 2005

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Abstract

In many automatic face recognition applications, a set of a personýs 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 densitiesconfined 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.