Konferenzbeitrag
Multi-algorithm Benchmark for Fingerprint Presentation Attack Detection with Laser Speckle Contrast Imaging
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2019
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
The increased usage of biometric authentication systems has raised concerns regarding the
security of components in a biometric system. As a consequence, preventing security issues related
to presentation attacks targeting the biometric capture device are of utmost importance. To develop
presentation attack detection (PAD) mechanisms, features confirming the liveness of the biometric
characteristic such as the blood flow within the finger are needed. Utilising laser speckle contrast
imaging (LSCI) to observe blood movement below the surface, we present an evaluation of different
machine learning classifiers for fingerprint PAD. The experiments over a database comprising 35
different presentation attack instrument (PAI) species show that the detection performance varies
depending on the utilised feature extraction method. A majority voting of selected classifiers and
features achieves an APCER of 9% for a convenient BPCER of 0.05%.