Face recognition is known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups. This work aims to learn a fair face representation, where faces of every group could be more equally represented. Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes. The adaptive module comprises kernel masks and channel-wise attention maps for each demographic group so as to activate different facial regions for identification, leading to more discriminative features pertinent to their demographics. Our introduced automated adaptation strategy determines whether to apply adaptation to a certain layer by iteratively computing the dissimilarity among demographic-adaptive parameters. A new de-biasing loss function is proposed to mitigate the gap of average intra-class distance between demographic groups. Experiments on face benchmarks (RFW, LFW, IJB-A, and IJB-C) show that our work is able to mitigate face recognition bias across demographic groups while maintaining the competitive accuracy.
翻译:众所周知,面部识别显示偏向,某些人口群体中的主体可以比其他群体得到更好的认识。这项工作旨在学习公平面貌代表,每个群体的脸面可以更平等地代表。我们提议的群体适应性分类师根据人口特征,利用适应性演进内核和对面关注机制,减轻偏见。适应性模块包括每个人口群体的内核面罩和频道关注图,以激活不同面部区域进行识别,从而导致与其人口构成有关的更具歧视性特征。我们采用的自动化适应战略决定了是否通过迭接计算人口适应性参数之间的差异,将适应性应用于某一层。提出了一个新的减少偏向性损失功能,以缩小各人口群体之间平均阶级间距离的差距。对面部基准的实验显示,我们的工作能够在保持竞争性准确性的同时,减少各人口群体之间面部识别的偏差。