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Fingervein Sample Image Quality Assessment using Natural Scene Statistics

dc.contributor.authorOliver Remy, Jutta Hämmerle-Uhl and Andreas Uhl
dc.contributor.editorBrömme, Arslan
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2022-10-27T10:19:34Z
dc.date.available2022-10-27T10:19:34Z
dc.date.issued2022
dc.description.abstractNatural Scene Statistics as used in non-reference image quality measures are proposed to be used as fingervein sample quality indicators. While NIQE and BRISQUE trained on common images with usual distortions do not work well in the fingervein quality context, their variants being trained on high and low quality fingervein sample data behave as expected from a biometric quality estimator. Experiments involve two publicly available fingervein datasets and two distinct template representations. The proposed (trained) quality measures are compared to a set of classical fingervein quality metrics which underlines their highly promising behaviour.en
dc.identifier.doi10.1109/BIOSIG55365.2022.9896974
dc.identifier.isbn978-3-88579-723-4
dc.identifier.pissn1617-5476
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39719
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-329
dc.subjectVascular biometrics
dc.subjectfingervein recognition
dc.subjectsample quality
dc.subjectnatural scene statistics
dc.titleFingervein Sample Image Quality Assessment using Natural Scene Statisticsen
dc.typeText/Conference Paper
gi.citation.endPage100
gi.citation.publisherPlaceBonn
gi.citation.startPage89
gi.conference.date14.-16. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleRegular Research Papers

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