Attack Detection for Finger and Palm Vein Biometrics by Fusion of Multiple Recognition Algorithms

Abstract

Vascular patterns in the hand region are not visible to the naked eye or consumer cameras, therefore finger and hand vein biometrics is often considered invulnerable to presentation attacks. However, one can never rule out the possibility that a malicious attacker manages to create functional attack samples. Various approaches on how to detect such attacks have been proposed, along with publicly available attack databases and varying ideas to create artificial attack samples. In a first step it is important to verify that created presentation attack artifacts hold the potential to deceive a real system. In order to provide a meaningful and comparable threat potential evaluation, this article evaluates 15 existing vein recognition schemes using attack samples derived from three finger vein attack databases and one palm vein attack database. As a second step, in this work we investigate an approach to combine these employed vein recognition schemes and utilizing them to perform presentation attack detection, which to the authors’ best knowledge has not been described in literature so far. Experimental results show that this approach can effectively be used to detect vein attack samples.