Effect of calibration data on forensic likelihood ratio from a face recognition system

A biometric system used for forensic evaluation requires a conversion of the score to a likelihood ratio. A likelihood ratio can be computed as the ratio of the probability of a score given the prosecution hypothesis is true and the probability of a score given the defense hypothesis is true. In this paper we study two different approaches of a forensic likelihood ratio computation in the context of forensic face recognition. These approaches differ in the databases they use to obtain the score distribution under the prosecution and the defense hypothesis and therefore consider slightly different interpretation of these hypotheses. The goal of this study is to quantify the effect of these approaches on the resultant likelihood ratio in the context of evidence evaluation from a face recognition system. A state-of-the art commercial face recognition system is employed for facial images comparison and computation of scores. A simple forensic case is simulated by randomly selecting a small subset from the FRGC database. Images in this subset are used to estimate the score distribution under the prosecution and the defense hypothesis and the effect of different approaches of a likelihood ratio computation is demonstrated and explained. It is observed that there is a significant variation in the resultant likelihood ratios given the databases which are used to model the prosecution and defense hypothesis are varied.