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Exploring Deep Learning Image Super-Resolution for Iris Recognition
University of Salzburg, Department of Computer Sciences, Salzburg, Austria & Federal University of Tocantins, Department of Computer Sciences, Tocantins, Brazil.
University of Salzburg, Department of Computer Sciences, Salzburg, Austria.ORCID iD: 0000-0002-5921-8755
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0002-1400-346X
University of Malta, Department of CCE, Msida, Malta.ORCID iD: 0000-0001-8106-9891
2017 (English)In: 25th European Signal Processing Conference (EUSIPCO 2017): 28 August-2 September 2017, Kos island, Greece, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 2017-January, p. 2176-2180Conference 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 information 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
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 2017-January, p. 2176-2180
Keywords [en]
Biometrics, Infrared devices, Learning systems, Neural networks, Optical resolving power, Quality of service, Signal processing, Convolutional neural network, High resolution image, Image super resolutions, Iris recognition, Learning approach, Learning methods, Local information, Quality assessment, Deep learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-40214DOI: 10.23919/EUSIPCO.2017.8081595ISI: 000426986000439Scopus ID: 2-s2.0-85041479673ISBN: 978-0-9928626-7-1 (electronic)ISBN: 978-0-9928626-8-8 (print)ISBN: 978-1-5386-0751-0 (print)OAI: oai:DiVA.org:hh-40214DiVA, id: diva2:1366066
Conference
25th European Signal Processing Conference (EUSIPCO 2017), Kos, Greece, 28 August - 2 September, 2017
Note

Funder: National Council for Scientific and Technological Development (CNPq) under grant No. 00736/2014-0

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28

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Alonso-Fernandez, Fernando

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