Modeling biometric systems using the general pareto distribution (GPD)

Abstract

Statistical modeling of biometric systems at the score level is extremely important. It is the foundation of the performance assessment of biometric systems including determination of confidence intervals and test sample size for simulations, and performance prediction of real world systems. Statistical modeling of multimodal biometric systems allows the development of a methodology to integrate information from multiple biometric sources. We present a novel approach for estimating the marginal biometric matching score distributions by using extreme value theory in conjunction with non-parametric methods. Extreme Value Theory (EVT) is based on the modeling of extreme events represented by data which has abnormally low or high values in the tails of the distributions. Our motivation stems from the observation that the tails of the biometric score distributions are often difficult to estimate using other methods due to lack of sufficient numbers of training samples. However, good estimates of the tails of biometric distributions are essential for defining the decision boundaries. We present EVT based novel procedures for fitting a score distribution curve. A general non-parametric method is used for fitting the majority part of the distribution curve, and a parametric EVT model - the general Pareto distribution - is used for fitting the tails of the curve. We also demonstrate the advantage of applying the EVT by experiments.