Nonparametric Statistical Data Analysis of Fingerprint Minutiae Exchange with Two-Finger Fusion
Jin Chu Wu, Michael D. Garris
A nonparametric inferential statistical data analysis is presented. The utility of this method is demonstrated through analyzing results from minutiae exchange with two-finger fusion. High-accuracy vendors are selected and mean error rates are compared between two modes of matching standard fingerprint templates: 1) Native Matching - where the same vendor generates the templates and the matcher, and 2) Scenario 1 Interoperability - where vendor A¿s enrollment template is matched to vendor B¿s authentication template using vendor B¿s matcher. The purpose of this analysis is to make inferences about the underlying population from sample data, which provide insights at an aggregate level. This is very different from the data analysis presented in the MINEX04 report in which vendors are individually ranked and compared. Using the nonparametric bootstrap bias-corrected and accelerated (BCa) method, 95% confidence intervals are computed for each mean error rate. Nonparametric significance tests are then applied to further determine if the difference between two underlying populations is real or by chance with a certain probability. Results from this method show that at a 95% confidence there is a significant degradation in accuracy of Scenario 1 Interoperability with respect to Native Matching with on average a two-fold increase in False Non-Match Rate. Additionally, it is proved why two-finger fusion using the sum rule is more accurate than single-finger matching under the same conditions. Results of a simulation using the nonparametric bootstrap are also presented to show the significance of the confidence intervals derived from the small size of samples, in our case, six vendors.
and Garris, M.
Nonparametric Statistical Data Analysis of Fingerprint Minutiae Exchange with Two-Finger Fusion, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.7376
(Accessed March 4, 2024)