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Iris Super-Resolution using CNNs: is Photo-Realism Important to Iris Recognition?
University of Salzburg, Salzburg, Austria & Federal University of Tocantins, Palmas, Brazil.
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
2018 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946Article in journal (Refereed) Epub ahead of print
Abstract [en]

The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in Iris Recognition nowadays. Concurrently, a great variety of single image Super-Resolution techniques are emerging, specially with the use of convolutional neural networks. The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimization of an objective function depending basically on the CNN architecture and the training approach. In this work, we explore single image Super-Resolution using CNNs for iris recognition. For this, we test different CNN architectures as well as the use of different training databases, validating our approach on a database of 1.872 near infrared iris images and on a mobile phone image database. We also use quality assessment, visual results and recognition experiments to verify if the photo-realism provided by the CNNs which have already proven to be effective for natural images can reflect in a better recognition rate for Iris Recognition. The results show that using deeper architectures trained with texture databases that provide a balance between edge preservation and the smoothness of the method can lead to good results in the iris recognition process. © The Institution of Engineering and Technology 2015

Place, publisher, year, edition, pages
Stevenage: Institution of Engineering and Technology, 2018.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-36650DOI: 10.1049/iet-bmt.2018.5146OAI: oai:DiVA.org:hh-36650DiVA, id: diva2:1199502
Note

Funding: CNPq-Brazil for Eduardo Ribeiro under grant No. 00736/2014-0.

Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-09-06

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Alonso-Fernandez, Fernando

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CiteExportLink to record
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  • apa
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