论文标题
减少面部识别的地理性能差异
Reducing Geographic Performance Differential for Face Recognition
论文作者
论文摘要
随着面部识别算法变得更加准确并被更广泛地部署，确保算法对每个人都同样效果均能奏效变得越来越重要。我们研究了不同国家 /地区的虚假接受和错误拒绝率的地理性能差异 - 当将自拍照与ID文档的照片进行比较时。我们展示了如何使用抽样策略来减轻地理性能差异，尽管数据集中发生了巨大失衡。使用香草域的适应策略来微调特定于域的DOCE-SELFIE数据上的面部识别CNN，从而提高了该模型在此类数据上的性能，但是，在存在不平衡的训练数据的情况下，也大大增加了人口统计学偏见。然后，我们通过采用抽样策略来平衡培训程序来显示如何减轻这种效果。
As face recognition algorithms become more accurate and get deployed more widely, it becomes increasingly important to ensure that the algorithms work equally well for everyone. We study the geographic performance differentials-differences in false acceptance and false rejection rates across different countries-when comparing selfies against photos from ID documents. We show how to mitigate geographic performance differentials using sampling strategies despite large imbalances in the dataset. Using vanilla domain adaptation strategies to fine-tune a face recognition CNN on domain-specific doc-selfie data improves the performance of the model on such data, but, in the presence of imbalanced training data, also significantly increases the demographic bias. We then show how to mitigate this effect by employing sampling strategies to balance the training procedure.
