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Impact of variations in synthetic training data on fingerprint classification

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2019

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Gesellschaft für Informatik e.V.

Zusammenfassung

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.

Beschreibung

İrtem, Pelin; İrtem, Emre; Erdoğmuş, Nesli (2019): Impact of variations in synthetic training data on fingerprint classification. BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-690-9. pp. 189-196. Further Conference Contributions. Darmstadt, Germany. 18.-20. September 2019

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