  Current face recognition systems achieve 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 mainly focused on learning less biased face
representations, which comes at the cost of a strongly degraded overall
recognition performance. In this work, we propose a novel unsupervised 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 three publicly available
datasets captured under controlled and in-the-wild circumstances. Results
demonstrate that our solution reduces demographic biases, e.g. by up to 82.7%
in the case when gender is considered. Moreover, it mitigates the bias more
consistently than existing works. In contrast to previous works, our fair
normalization approach enhances the overall performance by up to 53.2% at false
match rate of 0.001 and up to 82.9% at a false match rate of 0.00001.
Additionally, it is easily integrable into existing recognition systems and not
limited to face biometrics.
