Fairness-based parameter selection in multi-modal biometric authentication

A multi-modal biometric authentication system based on iterative fusion operator trees is considered, where the per-user performance is also depending on the order of biometric feature evaluations. Therefore, each user would have its own optimal choice of order parameters of the biometric system. However, for a system approach there should be only one general parametrization of the whole biometric system for all users, and the necessary consequence are performance wins and losses for each user. The common approach for this purpose is to use the parameter setting that maximizes the average performance over users. However, this does not necessarily reflect a good balance of performance wins and losses. In order to gain more insight into the corresponding trade-offs, and to facilitate an effective selection of an order parameter, the concept of fairness relations and their maximality, which has become a recent topic of many investigations in communication network resource sharing tasks, is applied to this problem of multi-modal biometric systems. The central idea of the paper is that by applying fairness relation to select the parameters, we obtain a system where subjects have a more fair chance of being right authenticated. A comparative study of an experimental test data set shows that maxmin fairness gives solutions with major drawbacks for the majority of users, while proportional fairness comes close to the maximum average.