论文标题
通过组自适应分类器缓解面部识别偏见
Mitigating Face Recognition Bias via Group Adaptive Classifier
论文作者
论文摘要
已知面部识别会表现出偏见 - 与其他群体相比，某个人群组中的受试者可以更好地识别。这项工作旨在学习公平的表现，每个小组的面孔都可以更加同样地表示。我们提出的组自适应分类器通过根据其人口统计学属性在面部上使用自适应卷积内核和注意力机制来减轻偏见。自适应模块包括每个人口组的内核面具和渠道注意图，以激活不同的面部区域以识别识别，从而导致与其人口统计有关的更具歧视性特征。我们引入的自动适应策略通过迭代计算人口自适应参数之间的差异来确定是否将适应性应用于某个层。提出了一种新的偏见损失函数，以减轻人口统计组之间平均阶层距离的差距。面对基准（RFW，LFW，IJB-A和IJB-C）的实验表明，我们的工作能够减轻跨人群群体的面部识别偏见，同时保持竞争精度。
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.
