Group-Specific Score Normalization for Biometric Systems

Abstract

The problem of biometric menagerie, first pointed out by Doddington et al. (1998), is one that plagues all biometric systems. They observe that only a handful of clients (enrolled users in the gallery) actually contribute disproportionately to recognition errors. While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies, in this study, we explore a novel category of approaches: group-specific score normalization. While client-specific score normalization can be negatively impacted by the paucity of genuine score samples, group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated. Experimental evidence based on face, fingerprint and iris modalities show that our proposal generally outperforms client-specific score normalization as well as the baseline systems (without any normalization) across all possible operating points (so obtained by changing the decision threshold).