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Exploring Deep Learning Image Super-Resolution for Iris Recognition
University of Salzburg, Salzburg, Austria.
University of Salzburg, Salzburg, Austria.
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.
2017 (English)In: 2017 25th European Signal Processing Conference (EUSIPCO 2017), 2017, 2240-2244 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local in-formation and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.  © EURASIP 2017

Place, publisher, year, edition, pages
2017. 2240-2244 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-34739ISBN: 978-0-9928626-7-1 (print)OAI: oai:DiVA.org:hh-34739DiVA: diva2:1133805
Conference
2017 25th European Signal Processing Conference (EUSIPCO 2017), Kos Island, Greece, August 28 - September 2, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Note

Funding: CNPq-Brazil

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-10-09Bibliographically approved

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fulltext(817 kB)22 downloads
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Alonso-Fernandez, Fernando

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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