Abstract
Bias has been a constant in face recognition models. Over the years, researchers have looked at it from both the model and the data point of view. However, their approach to mitigation of data bias was limited and lacked insight on the real nature of the problem. Here, in this document, we propose to revise our use of ethnicity labels as a continuous variable instead of a discrete value per identity. We validate our formulation both experimentally and theoretically, showcasing that not all identities from one ethnicity contribute equally to the balance of the dataset; thus, having the same number of identities per ethnicity does not represent a balanced dataset. We further show that models trained on datasets balanced in the continuous space consistently outperform models trained on data balanced in the discrete space. We trained more than 65 different models, and created more than 20 subsets of the original datasets.
Abstract (translated)
偏见一直是面部识别模型中的一个持续问题。多年来，研究人员从模型和数据两个角度对此进行了研究。然而，他们缓解数据偏差的方法是有限的，并且缺乏对问题本质的理解。在此文档中，我们提出将种族标签作为连续变量而非每个身份的一个离散值来使用。我们通过实验和理论验证了我们的方法，展示了来自同一族裔的不同身份对于数据集平衡的贡献并不相同；因此，拥有相同比例的各族裔身份并不代表一个均衡的数据集。此外，我们还证明在连续空间中平衡的数据集上训练出的模型始终优于在离散空间中平衡的数据集上训练出的模型。为了支持这一结论，我们训练了超过65种不同的模型，并创建了原始数据集的20多个子集。
URL
https://arxiv.org/abs/2506.01532
