Face recognition with image sets using manifold density divergence

Ognjen Arandjelovic*, Gregory Shakhnarovich, John Fisher, Roberto Cipolla, Trevor Darrell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

261 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
EditorsC. Schmid, S. Soatto, C. Tomasi
Pages581-588
Number of pages8
Volume1
DOIs
Publication statusPublished - 2005
Event2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States
Duration: 20 Jun 200525 Jun 2005

Conference

Conference2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Country/TerritoryUnited States
CitySan Diego, CA
Period20/06/0525/06/05

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