Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model. (arXiv:2210.13664v1 [cs.CV])

In spite of the high performance and reliability of deep learning algorithms
in a wide range of everyday applications, many investigations tend to show that
a lot of models exhibit biases, discriminating against specific subgroups of
the population (e.g. gender, ethnicity). This urges the practitioner to develop
fair systems with a uniform/comparable performance across sensitive groups. In
this work, we investigate the gender bias of deep Face Recognition networks. In
order to measure this bias, we introduce two new metrics, …

arxiv bias face face recognition gender gender bias