  Fair biometric algorithms have similar verification performance across
different demographic groups given a single decision threshold. Unfortunately,
for state-of-the-art face recognition networks, score distributions differ
between demographics. Contrary to work that tries to align those distributions
by extra training or fine-tuning, we solely focus on score post-processing
methods. As proved, well-known sample-centered score normalization techniques,
Z-norm and T-norm, do not improve fairness for high-security operating points.
Thus, we extend the standard Z/T-norm to integrate demographic information in
normalization. Additionally, we investigate several possibilities to
incorporate cohort similarities for both genuine and impostor pairs per
demographic to improve fairness across different operating points. We run
experiments on two datasets with different demographics (gender and ethnicity)
and show that our techniques generally improve the overall fairness of five
state-of-the-art pre-trained face recognition networks, without downgrading
verification performance. We also indicate that an equal contribution of False
Match Rate (FMR) and False Non-Match Rate (FNMR) in fairness evaluation is
required for the highest gains. Code and protocols are available.
