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Unsupervised Learning of Fingerprint Rotations

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dc.contributor.authorSchuch, Patrick
dc.contributor.authorMay, Jan Marek
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.accessioned2019-06-17T10:00:26Z
dc.date.available2019-06-17T10:00:26Z
dc.date.issued2018
dc.description.abstractThe alignment of fingerprint samples is a preprocessing step in fingerprint recognition. It allows an improved biometric feature extraction and a more accurate biometric comparison. We propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution is an unsupervised training strategy similar to Siamese Networks for estimation of rotations. The approach does not need any labelled data for training. It is trained to estimate orientation differences for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute deviation 2:1 on data similar to the training data, which supports the alignment task. For other datasets accuracies down to 6:2 mean absolute deviation are achieved.en
dc.identifier.isbn978-3-88579-676-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23803
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-282
dc.subjectfingerprint recognition
dc.subjectmachine learning
dc.subjectalignment
dc.subjectunsupervised learning.
dc.titleUnsupervised Learning of Fingerprint Rotationsen
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