Cost curve analysis of biometric system performance

Biometric classification algorithms typically offer a range of performance characteristics which balance false non-match and false match rates. Nevertheless, the threshold which meets application requirements is usually selected without explicit consideration of cost implications of misclassification. This paper presents the analysis of recognition performance of multiple face and fingerprint algorithms using cost curves. Cost curves allow the introduction of misclassification costs and prior probabilities of proportions of genuine and impostor classes in the selection of biometric system thresholds. The inclusion of misclassification costs and prior probabilities is important since they can either change with time, or with the location where the biometric system is deployed.