Face Recognition with Image Sets Using Manifold Density Divergence

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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Ognjen Arandjelovic, Gregory Shakhnarovich, John Fisher, Roberto Cipolla, Trevor Darrell
• Keywords: Automatic Face Recognition, Computer Vision, CVPR 2005, Probability Densities, Recognition Problem, Recognition Rate, Severe Illumination Variation