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Exploring Deep Lear...
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Ribeiro, EduardoUniversity of Salzburg, Department of Computer Sciences, Salzburg, Austria & Federal University of Tocantins, Department of Computer Sciences, Tocantins, Brazil
(författare)
Exploring Deep Learning Image Super-Resolution for Iris Recognition
- Artikel/kapitelEngelska2017
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Institute of Electrical and Electronics Engineers (IEEE),2017
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LIBRIS-ID:oai:DiVA.org:hh-40214
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https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-40214URI
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https://doi.org/10.23919/EUSIPCO.2017.8081595DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34739URI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:kon swepub-publicationtype
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Funder: National Council for Scientific and Technological Development (CNPq) under grant No. 00736/2014-0
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Funding: CNPq-Brazil
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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.
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Uhl, AndreasUniversity of Salzburg, Department of Computer Sciences, Salzburg, Austria
(författare)
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Alonso-Fernandez, Fernando,1978-Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS),CAISR Centrum för tillämpade intelligenta system (IS-lab)(Swepub:hh)feralo
(författare)
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Farrugia, Reuben A.University of Malta, Department of CCE, Msida, Malta
(författare)
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University of Salzburg, Department of Computer Sciences, Salzburg, Austria & Federal University of Tocantins, Department of Computer Sciences, Tocantins, BrazilUniversity of Salzburg, Department of Computer Sciences, Salzburg, Austria
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:25th European Signal Processing Conference (EUSIPCO 2017): Institute of Electrical and Electronics Engineers (IEEE), s. 2176-2180978099286267197809928626889781538607510
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