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Face Presentation Attack Detection in Ultraviolet Spectrum via Local and Global Features

: Siegmund, Dirk; Kerckhoff, Florian; Yeste Magdaleno, Javier; Jansen, Nils; Kirchbuchner, Florian; Kuijper, Arjan

Brömme, Arslan (Ed.); Busch, Christoph; Dantcheva, Antitza; Raja, Kiran; Rathgeb, Christian; Uhl, Andreas ; Gesellschaft für Informatik -GI-, Bonn; Bundesamt für Sicherheit in der Informationstechnik -BSI-, Bonn; European Association for Biometrics -EAB-; European Commission, Joint Research Centre -JRC-; TeleTrusT Deutschland e.V., Verein zur Förderung der Vertrauenswürdigkeit von Informations- und Kommunikationstechnik; Norwegian Biometrics Laboratory -NBL-; Fraunhofer-Institut für Graphische Datenverarbeitung -IGD-, Darmstadt:
BIOSIG 2020, 19th International Conference of the Biometrics Special Interest Group. Proceedings : 16.-18.09.2020, Fully Virtual Conference
Bonn: GI, 2020 (GI-Edition - Lecture Notes in Informatics (LNI). Proceedings P-306)
ISBN: 978-3-88579-700-5
Gesellschaft für Informatik, Special Interest Group on Biometrics and Electronic Signatures (BIOSIG International Conference) <19, 2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Fraunhofer IGD ()
Lead Topic: Smart City; Research Line: Computer vision (CV); Research Line: Machine Learning (ML); biometric identification systems; spoofing attacks; biometrics; security technologies; CRISP; ATHENE

The security of the commonly used face recognition algorithms is often doubted, as they appear vulnerable to so-called presentation attacks. While there are a number of detection methods that are using different light spectra to detect these attacks this is the first work to explore skin properties using the ultraviolet spectrum. Our multi-sensor approach consists of learning features that appear in the comparison of two images, one in the visible and one in the ultraviolet spectrum. We use brightness and keypoints as features for training, experimenting with different learning strategies. We present the results of our evaluation on our novel Face UV PAD database. The results of our method are evaluated in an leave-one-out comparison, where we achieved an APCER/BPCER of 0%/0.2%. The results obtained indicate that UV images in presentation attack detection include useful information that are not easy to overcome.