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The demographic disparity of biometric systems has led to serious concernsregarding their societal impact as well as applicability of such systems inprivate and public domains. A quantitative evaluation of demographic fairnessis an important step towards understanding, assessment, and mitigation ofdemographic bias in biometric applications. While few, existing fairnessmeasures are based on post-decision data (such as verification accuracy) ofbiometric systems, we discuss how pre-decision data (score distributions)provide useful insights towards demographic fairness. In this paper, weintroduce multiple measures, based on the statistical characteristics of scoredistributions, for the evaluation of demographic fairness of a genericbiometric verification system. We also propose different variants for eachfairness measure depending on how the contribution from constituent demographicgroups needs to be combined towards the final measure. In each case, thebehavior of the measure has been illustrated numerically and graphically onsynthetic data. The demographic imbalance in benchmarking datasets is oftenoverlooked during fairness assessment. We provide a novel weighing strategy toreduce the effect of such imbalance through a non-linear function of samplesizes of demographic groups. The proposed measures are independent of thebiometric modality, and thus, applicable across commonly used biometricmodalities (e.g., face, fingerprint, etc.).

Fairness Index Measures to Evaluate Bias in Biometric Recognition