An adversarial learning algorithm for mitigating gender bias in face recognition

State-of-the-art face recognition networks implicitly encode gender information while being trained for identity classification. Gender is often viewed as an important face attribute to recognize humans. But, the expression of gender information in deep facial features appears to contribute to gender bias in face recognition, i.e. we find a significant difference in the recognition accuracy of DCNNs on male and female faces. We hypothesize that reducing implicitly encoded gender information will help reduce this gender bias. Therefore, we present a novel approach called `Adversarial Gender De-biasing (AGD)' to reduce the strength of gender information in face recognition features. We accomplish this by introducing a bias reducing classification loss $L_{br}$. We show that AGD significantly reduces bias, while achieving reasonable recognition performance. The results of our approach are presented on two state-of-the-art networks.