Fairness in Biometrics: a figure of merit to assess biometric verification systems
This work addresses fairness issues in biometric systems, which is crucial for mitigating social impacts on minorities, though it appears incremental by proposing a new metric rather than a fundamental solution.
The authors tackled the problem of assessing fairness in biometric verification systems by introducing the Fairness Discrepancy Rate (FDR) as a figure of merit, and demonstrated its application through synthetic and real-world face biometric datasets involving gender and race demographics.
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.