Demographic biases exist in current models used for facial recognition (FR). Our Balanced Faces in the Wild (BFW) dataset is a proxy to measure bias across ethnicity and gender subgroups, allowing one to characterize FR performances per subgroup. We show that results are non-optimal when a single score threshold determines whether sample pairs are genuine or imposters. Furthermore, within subgroups, performance often varies significantly from the global average. Thus, specific error rates only hold for populations matching the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial features extracted from state-of-the-art neural networks, boosting the average performance. The proposed method also preserves identity information while removing demographic knowledge. The removal of demographic knowledge prevents potential biases from being injected into decision-making and protects privacy since demographic information is no longer available. We explore the proposed method and show that subgroup classifiers can no longer learn from the features projected using our domain adaptation scheme. For source code and data, see https://github.com/visionjo/facerec-bias-bfw.
翻译:目前用于面部识别的模型(FR)中存在着人口偏见。野生(BFW)数据集中的我们平衡面是一个用来衡量族裔和性别分组之间偏见的替代物,可以用来描述每个分组的FR表现特征。我们表明,当一个得分阈值确定抽样对子是否真实或假冒时,结果并不理想。此外,在分组内,业绩往往与全球平均数大不相同。因此,特定误差率只维持与验证数据相匹配的人口。我们利用从最新神经网络提取的面部特征的新版域适应学习计划来缓解不平衡的性能,提升平均性能。拟议方法还保存身份信息,同时消除人口学知识。人口学知识的消失防止了潜在偏差被注入到决策中,并保护隐私,因为人口信息不再可用。我们探讨拟议的方法,并表明分组分类者无法再从利用我们域适应计划预测的特征中学习。关于源码和数据,见https://github.com/visionjo/facerec-bis-bfw。)