Evaluation on Biometric Accuracy Estimation Using Generalized Pareto (GP) Distribution

Abstract

The accuracy of biometric authentication technology is becoming more sophisticated with its progress. For this reason, a huge number of biometric samples are required for accuracy evaluation, and the increased collection cost is an issue for biometric vendors. This work establishes a biometric accuracy estimation method using an extreme value theory to reduce the collection cost. It also explains the estimation procedure of false match rate using the generalized Pareto distribution and shows results applied to the face, gait, and voice comparison score data with an estimation effect of about 5–10 times. We investigate the criteria for the applicability of extremum statistics through application cases.