Second-level partition for estimating far confidence intervals in biometric systems

Abstract

Most biometric authentication algorithms make use of a similarity score that defines how similar two templates are according to a threshold and the accuracy of the results are expressed in terms of a False Reject Rate (FRR) or False Accept Rate (FAR) that is estimated using the training data set. A confidence interval is assigned to any claim of accuracy with 90% being commonly assumed for biometric-based authentication systems. However, these confidence intervals may not be as accurate as is presumed. In this paper, we report the results of experiments measuring the performance of the widely-used subset bootstrap approach to estimating the confidence interval of FAR. We find that the coverage of the FAR confidence intervals estimated by the subset bootstrap approach is reduced by the dependence between two similarities when they come from two individual pairs shared with a common individual. This is because subset bootstrap requires the independence of different subsets. To deal with this, we present a second-level partition to the similarity score set between different individuals, producing what we call a subset false accept rate (SFAR) bootstrap estimation. The experimental results show that the proposed procedures greatly increase the coverage of the FAR confidence intervals. © 2009 Springer Berlin Heidelberg.