Fingerprint recognition and matching is a common form of user authentication. While a fingerprint is unique to each individual, authentication is vulnerable when an attacker can forge a copy of the fingerprint (spoof). To combat these spoofed fingerprints, spoof detection and liveness detection algorithms are currently being researched as countermeasures to this security vulnerability. This paper introduces a fingerprint anti-spoofing mechanism using machine learning.
@article{arxiv.2304.05312,
title = {Fingerprint Liveness Detection using Minutiae-Independent Dense Sampling of Local Patches},
author = {Riley Kiefer and Jacob Stevens and Ashok Patel},
journal= {arXiv preprint arXiv:2304.05312},
year = {2023}
}
Comments
Submitted, peer-reviewed, accepted, and under publication with Springer Nature