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Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks

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dc.contributor.authorOsorio-Roig, Dailé
dc.contributor.authorRathgeb, Christian
dc.contributor.authorGomez-Barrero, Marta
dc.contributor.authorMorales-González, Annette
dc.contributor.authorGarea-Llano, Eduardo
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:22Z
dc.date.available2019-06-17T10:00:22Z
dc.date.issued2018
dc.description.abstractIris segmentation under visible wavelengths (VWs) is a vital processing step for iris recognition systems operating at-a-distance or in non-cooperative environments. In these scenarios the presence of various artefacts, e.g. occlusions or specular reflections, as well as out-of-focus blur represents a significant challenge. The vast majority of proposed iris segmentation algorithms under VW aim at discriminating the iris and non-iris regions without taking into account the variability that is present in the non-iris region. In this paper, we introduce the idea of segmenting the iris region using a multi-class approach which differentiates additional classes, e.g. pupil or sclera, as opposed to commonly employed bi-class approaches (iris and non-iris). Experimental results conducted on two publicly available databases show that the use of the proposed multi-class approach improves the iris segmentation accuracy. Simultaneously, it also allows for the segmentation of different non-iris regions, e.g. glasses, which could be employed in further application scenarios.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5469
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23795
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectBiometrics
dc.subjectiris recognition
dc.subjectsemantic segmentation
dc.subjectfully convolutional networks.
dc.titleVisible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networksen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group