Exploring gender prediction from iris biometrics
| dc.contributor.author | Fairhurst, Michael | |
| dc.contributor.author | Erbilek, Meryem | |
| dc.contributor.author | Da Costa-Abreu, Márjory | |
| dc.contributor.editor | Brömme, Arslan | |
| dc.contributor.editor | Busch, Christoph | |
| dc.contributor.editor | Rathgeb, Christian | |
| dc.contributor.editor | Uhl, Andreas | |
| dc.date.accessioned | 2017-06-30T08:19:14Z | |
| dc.date.available | 2017-06-30T08:19:14Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Prediction of gender characteristics from iris images has been investigated and some successful results have been reported in the literature, but without considering performance for different iris features and classifiers. This paper investigates for the first time an approach to gender prediction from iris images using different types of features (including a small number of very simple geometric features, texture features and a combination of geometric and texture features) and a more versatile and intelligent classifier structure. Our proposed approaches can achieve gender prediction accuracies of up to 90\% in the BioSecure Database. | en |
| dc.identifier.isbn | 978-3-88579-639-8 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2015 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-245 | |
| dc.title | Exploring gender prediction from iris biometrics | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 230 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 223 | |
| gi.conference.date | 9.-11. September 2015 | |
| gi.conference.location | Darmstadt |
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