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. The analysis focused on high-accuracy vendors and 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 greater-than-95 % confidence level there is a significant degradation in accuracy of Scenario 1 Interoperability with respect to Native Matching. The difference of error rates can reach 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 singlefinger matching under the same conditions. Results of a simulation are also presented to show the significance of the confidence intervals derived from the small size of samples, such as six error rates in some of our cases.
Proceedings Title: Proceedings on SPIE Conference
Conference Dates: April 9-13, 2007
Conference Location: Orlando, FL
Pub Type: Conferences
bootstrap, fingerprint matching, inferential, interoperability, minutiae exchange, nonparametric, significance test, standard templates, statistical data analysis