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Impact of variations in synthetic training data on fingerprint classification

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dc.contributor.authorİrtem, Pelin
dc.contributor.authorİrtem, Emre
dc.contributor.authorErdoğmuş, Nesli
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-15T13:01:27Z
dc.date.available2020-09-15T13:01:27Z
dc.date.issued2019
dc.description.abstractCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.en
dc.identifier.isbn978-3-88579-690-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34229
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 classification
dc.subjectsynthetic ground truth
dc.subjectdeep learning
dc.titleImpact of variations in synthetic training data on fingerprint classificationen
dc.typeText/Conference Paper
gi.citation.endPage196
gi.citation.publisherPlaceBonn
gi.citation.startPage189
gi.conference.date18.-20. September 2019
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleFurther Conference Contributions

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