White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations
This work addresses the problem of evaluating fingerprint matching modules for researchers and developers in biometrics, though it is incremental as it builds on limited prior white-box studies.
The study conducted a white-box evaluation of fingerprint minutiae matchers to assess their robustness against controlled perturbations like random noise and non-linear distortions, revealing that performance is more susceptible to non-linear distortion and missing minutiae than to spurious minutiae or small positional displacements.
Prevailing evaluations of fingerprint recognition systems have been performed as end-to-end black-box tests of fingerprint identification or authentication accuracy. However, performance of the end-to-end system is subject to errors arising in any of its constituent modules, including: fingerprint scanning, preprocessing, feature extraction, and matching. Conversely, white-box evaluations provide a more granular evaluation by studying the individual sub-components of a system. While a few studies have conducted stand-alone evaluations of the fingerprint reader and feature extraction modules of fingerprint recognition systems, little work has been devoted towards white-box evaluations of the fingerprint matching module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) minutiae-based matchers in terms of their robustness against controlled perturbations (random noise and non-linear distortions) introduced into the input minutiae feature sets. Our white-box evaluations reveal that the performance of fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations.