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Exploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolution

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dc.contributor.authorRibeiro,Eduardo
dc.contributor.authorUhl,Andreas
dc.contributor.editorBrömme,Arslan
dc.contributor.editorBusch,Christoph
dc.contributor.editorDantcheva,Antitza
dc.contributor.editorRathgeb,Christian
dc.contributor.editorUhl,Andreas
dc.date.accessioned2017-09-26T09:21:00Z
dc.date.available2017-09-26T09:21:00Z
dc.date.issued2017
dc.description.abstractIncreasingly, iris recognition towards more relaxed conditions has issued a new superresolution field direction. In this work we evaluate the use of deep learning and transfer learning for single image super resolution applied to iris recognition. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. The good results obtained by the texture transfer learning using a deep architecture suggest that features learned by Convolutional Neural Networks used for image super-resolution can be highly relevant to increase iris recognition rate.en
dc.identifier.isbn978-3-88579-664-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/4648
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBIOSIG 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-70
dc.subjectSingle-Image Super Resolution
dc.subjectIris Recognition
dc.subjectTransfer Learning
dc.subjectConvolutional Neural Networks
dc.titleExploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolutionen
gi.citation.endPage202
gi.citation.startPage195
gi.conference.date20.-22. September 2017
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleFurther Conference Contributions

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P270 - BIOSIG 2017 - Proceedings of the 16th International Conference of the Biometrics Special Interest Group