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Quality driven iris recognition improvement

dc.contributor.authorCremer, Sandra
dc.contributor.authorLemperiere, Nadege
dc.contributor.authorDorizzi, Bernadette
dc.contributor.authorGarcia-Salicetti, Sonia
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.date.accessioned2018-10-31T12:34:05Z
dc.date.available2018-10-31T12:34:05Z
dc.date.issued2013
dc.description.abstractThe purpose of the work presented in this paper is to adapt the feature extraction and matching steps of iris recognition to the quality of the input images. To this end we define a GMM-based global quality metric associated to a pair of normalized iris images. It quantifies the amount of artifact in these images as well as the amount of texture in artifact-free regions. First we use this metric to adjust, for each pair of irises, the proportion of the normalized image selected on a local quality criteria for feature extraction. This approach is tested with two matching techniques: one performs a bit to bit comparison of binary feature vectors and the other one computes local cross-correlations between real valued vectors. We show that our approach is effective with both techniques. Then we use our metric to choose the matching technique that is best adapted to each image pair in order to make a good compromise between accuracy and speed.en
dc.identifier.isbn978-3-88579-606-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/17694
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2013
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-212
dc.titleQuality driven iris recognition improvementen
dc.typeText/Conference Paper
gi.citation.endPage98
gi.citation.publisherPlaceBonn
gi.citation.startPage87
gi.conference.date04.-06. September 2013
gi.conference.locationDarmstadt
gi.conference.sessiontitleRegular Research Papers

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