Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
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Current face recognition systems achieved high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Consequently, an easily integrable solution is needed to reduce the discriminatory effect of these biased systems. Previous work introduced fairness-enhancing solutions that strongly degrades the overall system performance. In this work, we propose a novel fair score normalization approach that is specifically designed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost. Our hypothesis is built on the notation of individual fairness by designing a normalization approach that leads to treating "similar" individuals "similarly". Experiments were conducted on two publicly available datasets captured under controlled and in-the-wild circumstances. The results show that our fair normalization approach enhances the overall performance by up to 14.8 settings and up to 30.7 significantly reduces the errors of all demographic groups, and thus reduce bias. Especially under in-the-wild conditions, we demonstrated that our fair normalization method improves the recognition performance of the effected population sub-groups by 31.6 fairness-enhancing solution does not require demographic information about the individuals, leads to an overall performance boost, and can be easily integrated in existing biometric systems.
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