Image deblurring for less intrusive iris capture

Xinyu Huang, Liu Ren, Ruigang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

For most iris capturing scenarios, captured iris images could easily blur when the user is out of the depth of field (DOF) of the camera, or when he or she is moving. The common solution is to let the user try the capturing process again as the quality of these blurred iris images is not good enough for recognition. In this paper, we propose a novel iris deblurring algorithm that can be used to improve the robustness and nonintrusiveness for iris capture. Unlike other iris deblurring algorithms, the key feature of our algorithm is that we use the domain knowledge inherent in iris images and iris capture settings to improve the performance, which could be in the form of iris image statistics, characteristics of pupils or highlights, or even depth information from the iris capturing system itself. Our experiments on both synthetic and real data demonstrate that our deblurring algorithm can significantly restore blurred iris patterns and therefore improve the robustness of iris capture.

Original languageEnglish
Title of host publication2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Pages1558-1565
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Conference

Conference2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Country/TerritoryUnited States
CityMiami, FL
Period6/20/096/25/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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