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A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

Arandjelovic, Ognjen and Cipolla, R. 2007, A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution, in ICCV 2007 : Proceedings of the International Conference on Computer Vision 2007, IEEE, Piscataway, New Jersey, pp. 1-8.

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Title A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution
Author(s) Arandjelovic, Ognjen
Cipolla, R.
Conference name International Conference on Computer Vision (2007 : Rio de Janeiro, Brazil)
Conference location Rio de Janeiro, Brazil
Conference dates 14-20 Oct. 2007
Title of proceedings ICCV 2007 : Proceedings of the International Conference on Computer Vision 2007
Editor(s) [Unknown]
Publication date 2007
Conference series IEEE International Conference on Computer Vision
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, New Jersey
Summary We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.
Language eng
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 ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058430

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.