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Machine learning-based (ML) systems are being largely deployed since the lastdecade 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 thespotlight due to the social impact that this can get in minorities. In thiswork aspects of fairness in biometrics are addressed. First, we introduce thefirst figure of merit that is able to evaluate and compare fairness aspectsbetween multiple biometric verification systems, the so-called FairnessDiscrepancy Rate (FDR). A use case with two synthetic biometric systems isintroduced and demonstrates the potential of this figure of merit in extremecases of fair and unfair behavior. Second, a use case using face biometrics ispresented where several systems are evaluated compared with this new figure ofmerit using three public datasets exploring gender and race demographics.

Fairness in Biometrics: a figure of merit to assess biometric verification systems