Konferenzbeitrag
Impact of variations in synthetic training data on fingerprint classification
Lade...
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2019
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
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.