Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

Full Abstract

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Author information

Author/s: Vatsa, Mayank (M); Singh, Richa (R); Noore, Afzel (A);

Affiliation: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6109, USA. mayankv(-atsign-)csee.wvu.edu

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society (IEEE Trans Syst Man Cybern B Cybern), published in United States. (Language: eng)

Reference: 2008-Aug; vol 38 (issue 4) : pp 1021-35

Dates: Created 2008/07/17; Completed 2008/08/28;

PMID: 18632394, status: MEDLINE (last retrieved date: 2/18/2009)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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