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Face recognition with image sets using manifold density divergence

Arandjelovic, Ognjen, Shakhnarovich, G, Fisher, J, Cipolla, R and Darrell, T 2005, Face recognition with image sets using manifold density divergence, in CVPR 2005 : Proceedings of the Computer Vision and Pattern Recognition Conference 2005, IEEE, Piscataway, New Jersey, pp. 581-588, doi: 10.1109/CVPR.2005.151.

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Title Face recognition with image sets using manifold density divergence
Author(s) Arandjelovic, Ognjen
Shakhnarovich, G
Fisher, J
Cipolla, R
Darrell, T
Conference name Computer Vision and Pattern Recognition Conference (2005 : San Diego, California
Conference location San Diego, California
Conference dates 20-25 June 2005
Title of proceedings CVPR 2005 : Proceedings of the Computer Vision and Pattern Recognition Conference 2005
Editor(s) [Unknown]
Publication date 2005
Conference series Computer Vision and Pattern Recognition Conference
Start page 581
End page 588
Total pages 8
Publisher IEEE
Place of publication Piscataway, New Jersey
Summary 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.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769523722
Language eng
DOI 10.1109/CVPR.2005.151
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058434

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.