Detecting Double-Identity Fingerprint Attacks

Abstract

Double-identity biometrics, that is the combination of two subjects’ features into a single template, was demonstrated to be a serious threat against existing biometric systems. In fact, well-synthetized samples can fool state-of-the-art biometric verification systems, leading them to falsely accept both the contributing subjects. This work proposes one of the first techniques to defy existing double-identity fingerprint attacks. The proposed approach inspects the regions where the two aligned fingerprints overlap but minutiae cannot be consistently paired. If the quality of these regions is good enough to minimize the risk of false or miss minutiae detection, then the alarm score is increased. Experimental results carried out on two fingerprint databases, with two different techniques to generate double-identity fingerprints, validate the effectiveness of the proposed approach.