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Accurate and efficient face recognition from video

Arandjelovic, Ognjen 2010, Accurate and efficient face recognition from video, in BMVC 2010 : Proceedings of the 21st British machine vision association conference 2010, BMVA Press, Manchester, England, pp. 1-10, doi: 10.5244/C.24.64..

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Title Accurate and efficient face recognition from video
Author(s) Arandjelovic, Ognjen
Conference name British Machine Vision Conference (21st : 2010 : Aberystwyth, Wales)
Conference location Aberystwyth, Wales
Conference dates 31 Aug.-3 Sept. 2010
Title of proceedings BMVC 2010 : Proceedings of the 21st British machine vision association conference 2010
Editor(s) [Unknown]
Publication date 2010
Conference series British Machine Vision Conference
Start page 1
End page 10
Total pages 10
Publisher BMVA Press
Place of publication Manchester, England
Keyword(s) computer based face recognition
generic shape-illumination
Summary As a problem of high practical appeal but outstanding challenges, computer-based face recognition remains a topic of extensive research attention. In this paper we are specifically interested in the task of identifying a person from multiple training and query images. Thus, a novel method is proposed which advances the state-of-the-art in set based face recognition. Our method is based on a previously described invariant in the form of generic shape-illumination effects. The contributions include: (i) an analysis of computational demands of the original method and a demonstration of its practical limitations, (ii) a novel representation of personal appearance in the form of linked mixture models in image and pose-signature spaces, and (iii) an efficient (in terms of storage needs and matching time) manifold re-illumination algorithm based on the aforementioned representation. An evaluation and comparison of the proposed method with the original generic shape-illumination algorithm shows that comparably high recognition rates are achieved on a large data set (1.5% error on 700 face sets containing 100 individuals and extreme illumination variation) with a dramatic improvement in matching speed (over 700 times for sets containing 1600 faces) and storage requirements (independent of the number of training images).
ISBN 1901725405
Language eng
DOI 10.5244/C.24.64.
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 ©2010, BMVA Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058423

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.