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A new look at filtering techniques for illumination invariance in automatic face recognition

Arandjelovic, Ognjen and Cipolla, R. 2006, A new look at filtering techniques for illumination invariance in automatic face recognition, in FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition 2006, IEEE, Piscataway, New Jersey, pp. 449-454, doi: 10.1109/FGR.2006.14.

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Title A new look at filtering techniques for illumination invariance in automatic face recognition
Author(s) Arandjelovic, Ognjen
Cipolla, R.
Conference name International Conference on Automatic Face & Gesture Recognition (7th : 2006 : Amsterdam, The Netherlands)
Conference location Southampton, England
Conference dates 2-6 April 2006
Title of proceedings FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition 2006
Editor(s) [Unknown]
Publication date 2006
Conference series Automatic Face and Gesture Recognition Conference
Start page 449
End page 454
Total pages 6
Publisher IEEE
Place of publication Piscataway, New Jersey
Keyword(s) image processing filters
illumination conditions
algorithms
Summary Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals.
ISBN 0769525032
9780769525037
Language eng
DOI 10.1109/FGR.2006.14
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 ©2006, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058441

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.