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@ARTICLE{deFreitasPereira_TBIOM_2021,
         author = {de Freitas Pereira, Tiago and Marcel, S{\'{e}}bastien},
       projects = {Idiap},
          title = {Fairness in Biometrics: a figure of merit to assess biometric verification systems},
        journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
           year = {2021},
            doi = {10.1109/TBIOM.2021.3102862},
       abstract = {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 some social groups. In this work aspects of fairness in biometrics are addressed. First, we introduce a 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 demographic differentials. 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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2022/deFreitasPereira_TBIOM_2021.pdf}
}