Fingerprint recognition with embedded presentation attacks detection: are we ready?

• Main Authors: Micheletto, Marco, Marcialis, Gian Luca, Orrù, Giulia, Roli, Fabio
• Format: Journal Article
• Language: English
• Published: 20-10-2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Cryptography and Security
• DOI: 10.48550/arxiv.2110.10567

IEEE Transactions on Information Forensics and Security (2021) The diffusion of fingerprint verification systems for security applications
makes it urgent to investigate the embedding of software-based presentation
attack detection algorithms (PAD) into such systems. Companies and institutions
need to know whether such integration would make the system more "secure" and
whether the technology available is ready, and, if so, at what operational
working conditions. Despite significant improvements, especially by adopting
deep learning approaches to fingerprint PAD, current research did not state
much about their effectiveness when embedded in fingerprint verification
systems. We believe that the lack of works is explained by the lack of
instruments to investigate the problem, that is, modeling the cause-effect
relationships when two non-zero error-free systems work together. Accordingly,
this paper explores the fusion of PAD into verification systems by proposing a
novel investigation instrument: a performance simulator based on the
probabilistic modeling of the relationships among the Receiver Operating
Characteristics (ROC) of the two individual systems when PAD and verification
stages are implemented sequentially. As a matter of fact, this is the most
straightforward, flexible, and widespread approach. We carry out simulations on
the PAD algorithms' ROCs submitted to the most recent editions of LivDet
(2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger
12 matchers. Reported experiments explore significant scenarios to get the
conditions under which fingerprint matching with embedded PAD can improve,
rather than degrade, the overall personal verification performance.