Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks
dc.contributor.author | Osorio-Roig, Dailé | |
dc.contributor.author | Rathgeb, Christian | |
dc.contributor.author | Gomez-Barrero, Marta | |
dc.contributor.author | Morales-González, Annette | |
dc.contributor.author | Garea-Llano, Eduardo | |
dc.contributor.author | Busch, Christoph | |
dc.contributor.editor | Brömme, Arslan | |
dc.contributor.editor | Busch, Christoph | |
dc.contributor.editor | Dantcheva, Antitza | |
dc.contributor.editor | Rathgeb, Christian | |
dc.contributor.editor | Uhl, Andreas | |
dc.date.accessioned | 2019-06-17T10:00:22Z | |
dc.date.available | 2019-06-17T10:00:22Z | |
dc.date.issued | 2018 | |
dc.description.abstract | Iris 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.isbn | 978-3-88579-676-4 | |
dc.identifier.pissn | 1617-5469 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/23795 | |
dc.language.iso | en | |
dc.publisher | Köllen Druck+Verlag GmbH | |
dc.relation.ispartof | BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-283 | |
dc.subject | Biometrics | |
dc.subject | iris recognition | |
dc.subject | semantic segmentation | |
dc.subject | fully convolutional networks. | |
dc.title | Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks | en |
dc.type | Text/Conference Paper | |
gi.citation.publisherPlace | Bonn | |
gi.conference.date | 26.-28. September 2018 | |
gi.conference.location | Darmstadt |
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