Face recognition with image sets using manifold density divergence - Arandjelovic, O.; Shakhnarovich, G.; Fisher, J.; Cipolla, R.; Darrell, T. (2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005)
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.