Published studies have suggested the bias of automated face-based gender
classification algorithms across gender-race groups. Specifically, unequal
accuracy rates were obtained for women and dark-skinned people. To mitigate the
bias of gender classifiers, the vision community has developed several
strategies. However, the efficacy of these mitigation strategies is
demonstrated for a limited number of races mostly, Caucasian and
African-American. Further, these strategies often offer a trade-off between
bias and classification accuracy. To further advance the state-of-the-art, we
leverage the power of generative views, structured learning, and evidential
learning towards mitigating gender classification bias. We demonstrate the
superiority of our bias mitigation strategy in improving classification
accuracy and reducing bias across gender-racial groups through extensive
experimental validation, resulting in state-of-the-art performance in intra-
and cross dataset evaluations.

使用生成式视图、结构化学习和证据学习的方法，可以有效减少面部识别算法的性别分类偏差，并提升性能。