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 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.
Cite
Text
Arandjelovic et al. "Face Recognition with Image Sets Using Manifold Density Divergence." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.151Markdown
[Arandjelovic et al. "Face Recognition with Image Sets Using Manifold Density Divergence." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/arandjelovic2005cvpr-face/) doi:10.1109/CVPR.2005.151BibTeX
@inproceedings{arandjelovic2005cvpr-face,
title = {{Face Recognition with Image Sets Using Manifold Density Divergence}},
author = {Arandjelovic, Ognjen and Shakhnarovich, Gregory and Fisher, John and Cipolla, Roberto and Darrell, Trevor},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {581-588},
doi = {10.1109/CVPR.2005.151},
url = {https://mlanthology.org/cvpr/2005/arandjelovic2005cvpr-face/}
}