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Estimating the Confidence Interval of Expected Performance Curve in Biometric Authentication Using Joint Bootstrap

Evaluating biometric authentication performance is a complex task because the performance depends on the user set size, composition and the choice of samples. We propose to reduce the performance dependency of these three factors by deriving appropriate confidence intervals. In this study, we focus on deriving a confidence region based on the recently proposed Expected Performance Curve (EPC). An EPC is different from the conventional DET or ROC curve because an EPC assumes that the test class-conditional (client and impostor) score distributions are unknown and this includes the choice of the decision threshold for various operating points. Instead, an EPC selects thresholds based on the training set and applies them on the test set. The proposed technique is useful, for example, to quote realistic upper and lower bounds of the decision cost function used in the NIST annual speaker evaluation. Our findings, based on the 24 systems submitted to the NIST2005 evaluation, show that the confidence region obtained from our proposed algorithm can correctly predict the performance of an unseen database with two times more users with an average coverage of 95% (over all the 24 systems). A coverage is the proportion of the unseen EPC covered by the derived confidence interval.

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