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Fingerprint Pre-Alignment based on Deep Learning

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dc.contributor.authorDieckmann, Benjamin
dc.contributor.authorMerkle, Johannes
dc.contributor.authorRathgeb, Christian
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-15T13:01:31Z
dc.date.available2020-09-15T13:01:31Z
dc.date.issued2019
dc.description.abstractRobust fingerprint pre-alignment is vital for identification systems and biometric cryptosystems based on fingerprint minutiae, where computation of a relative alignment by comparison of the fingerprints is inefficient or intractable, respectively. The pre-alignment is achieved through an absolute alignment, i. e. an alignment computed for each fingerprint independently, which can be applied for fingerprint registration to compensate for variations in the placement (translation) and rotation of the fingerprints prior to their comparison. In this work, a deep learning approach for absolute pre-alignment of fingerprints is presented. The proposed algorithm employs a siamese network (with CNNs as subnetworks) which is trained on synthetically generated fingerprints using horizontal/vertical translation and rotation as three regression coefficients. Evaluations are conducted on the FVC2000 DB2a and the MCYT fingerprint database. Compared to other published fingerprint pre-alignment methods, the presented scheme achieves higher accuracy w. r. t. rotation estimation and overall robustness. In addition, the proposed pre-alignment is applied as a pre-processing step in a Fuzzy Vault scheme.en
dc.identifier.isbn978-3-88579-690-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34241
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-297
dc.subjectFingerprint Registration
dc.subjectDeep Learning
dc.subjectFingerprint Pre-Alignment
dc.subjectBiometric Template Protection
dc.titleFingerprint Pre-Alignment based on Deep Learningen
dc.typeText/Conference Paper
gi.citation.endPage93
gi.citation.publisherPlaceBonn
gi.citation.startPage83
gi.conference.date18.-20. September 2019
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleRegular Research Papers

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P296 - BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group