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Gradient edge map features for frontal face recognition under extreme illumination changes

Arandjelovic, Ognjen 2012, Gradient edge map features for frontal face recognition under extreme illumination changes, in BMVC 2012 : Proceedings of the British machine vision association conference, BMVA Press, [Surrey, England], pp. 1-11, doi: 10.5244/C.26.12.

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Title Gradient edge map features for frontal face recognition under extreme illumination changes
Author(s) Arandjelovic, Ognjen
Conference name British Machine Vision. Conference (2012 : Surrey, England)
Conference location Surrey, England
Conference dates 3-7 Sept. 2012
Title of proceedings BMVC 2012 : Proceedings of the British machine vision association conference
Editor(s) Bowden, Richard
Collomosse, John
Mikolajczyk, Krystian
Publication date 2012
Conference series British Machine Vision Conference
Start page 1
End page 11
Total pages 11
Publisher BMVA Press
Place of publication [Surrey, England]
Summary Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%.
ISBN 01901725464
Language eng
DOI 10.5244/C.26.12
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 ©2012, BMVA Press
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058428

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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.