Score calibration for optimal biometric identification

Abstract

We present a calibration algorithm that converts biometric matching scores into probability-based confidence scores. Using the context of iris biometrics, we show - theoretically and by experiments - that in addition to attaching a meaningful confidence measure to the output, this calibration technique yields the best possible detection error trade-off (DET) curves, both at the score level and at the decision level, thus maximizing the overall performance of the biometric system. © 2010 Springer-Verlag Berlin Heidelberg.