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Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches
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 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme, Christoph Busch, Antitza Dantcheva, Christian Rathgeb & Andreas Uhl, Bonn: Gesellschaft für Informatik, 2017, Vol. P-270, article id 8053512Conference paper, Published paper (Refereed)
Abstract [en]

Relaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities. © 2017 Gesellschaft fuer Informatik.

Place, publisher, year, edition, pages
Bonn: Gesellschaft für Informatik, 2017. Vol. P-270, article id 8053512
Series
Lecture Notes in Informatics (LNI) - Proceedings, ISSN 1617-5468 ; P-270
Keyword [en]
Iris, biometrics, super-resolution, low resolution
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-34738DOI: 10.23919/BIOSIG.2017.8053512Scopus ID: 2-s2.0-85034572701ISBN: 978-3-88579-664-0 (electronic)ISBN: 978-1-5386-0396-3 (print)OAI: oai:DiVA.org:hh-34738DiVA: diva2:1133802
Conference
16th International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, September 20-22, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-12-11Bibliographically approved

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Alonso-Fernandez, FernandoBigun, Josef

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CiteExportLink to record
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