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Iris Super-Resolution Using Iterative Neighbor Embedding
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
University of Malta, Msida, Malta.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
2017 (English)In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, 655-663 p.Conference paper, Published paper (Refereed)
Abstract [en]

Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. © 2017 IEEE

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2017. 655-663 p.
Keyword [en]
Iris recognition, Image reconstruction, Image resolution, Manifolds, Training, Databases, Iris
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-33864DOI: 10.1109/CVPRW.2017.94Scopus ID: 2-s2.0-85030244663ISBN: 978-1-5386-0733-6 (electronic)ISBN: 978-1-5386-0734-3 (print)OAI: oai:DiVA.org:hh-33864DiVA: diva2:1096742
Conference
International Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society Workshop on Biometrics, Hawaii Convention Center HI, USA, 21-26 Jul, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUSKnowledge Foundation, CAISR
Available from: 2017-05-18 Created: 2017-05-18 Last updated: 2017-12-14Bibliographically approved

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Alonso-Fernandez, FernandoBigun, Josef

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  • apa
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