Machine learning-based (ML) systems are being largely deployed since the last
decade in a myriad of scenarios impacting several instances in our daily lives.
With this vast sort of applications, aspects of fairness start to rise in the
spotlight due to the social impact that this can get in minorities. In this
work aspects of fairness in biometrics are addressed. First, we introduce the
first figure of merit that is able to evaluate and compare fairness aspects
between multiple biometric verification systems, the so-called Fairness
Discrepancy Rate (FDR). A use case with two synthetic biometric systems is
introduced and demonstrates the potential of this figure of merit in extreme
cases of fair and unfair behavior. Second, a use case using face biometrics is
presented where several systems are evaluated compared with this new figure of
merit using three public datasets exploring gender and race demographics.Comment: 11 page
