Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11755
Title: Impact of variations in synthetic training data on fingerprint classification
Authors: İrtem, Pelin
İrtem, Emre
Erdoğmuş, Nesli
Keywords: Fingerprint classification
Synthetic ground truth
Deep learning
Issue Date: 2019
Publisher: IEEE
Abstract: Creating 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.
Description: International Conference of the Biometrics-Special-Interest-Group (BIOSIG) -- SEP 18-20, 2019 -- Darmstadt, GERMANY -- Gesellschaft Informatik e V, Biometr Special Interest Grp, Gesellschaft Informatik e V, Competence Ctr Appl Secur Technol e V, German Fed Off Informat Secur, European Assoc Biometr, TeleTrusT Deutschland e V, Norwegian Biometr Lab, European Commiss Joint Res Ctr, Inst Engn & Technol Biometr Journal, Fraunhofer Inst Comp Graph Res, Ctr Res Secur & Privacy, Inst Elect & Elect Engineers
URI: https://hdl.handle.net/11147/11755
ISBN: 978-3-88579-690-9
ISSN: 1617-5468
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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