Mitigating MasterPrint vulnerability by employing minutiae geometry

Abstract

The biometric system accepts a fingerprint of an individual to authenticate, authorize, or identify a person. However, some devices with a small sensor use a partial fingerprint as input. Recent research has shown that partial fingerprints are not unique compared to the full fingerprint, leading to the MasterPrint vulnerability. A MasterPrint is a partial fingerprint identifying at least 4% distinct subjects enrolled with the database. An adversary exploits such MasterPrints for executing financial frauds, mounting a presentation attack, or criminal activities. Our work proposes minutiae geometry-based partial fingerprint identification method targeted to alleviate the MasterPrint vulnerability. The experiments comprise partial datasets created from five full-fingerprint benchmark datasets and six existing approaches to compare the results of the proposed method. The observations show that the proposed approach achieved up to 97% identification accuracy while generating merely 0.1% MasterPrints.