Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias towards certain demographics. This bias is inflicted both by limited diversity across demographics in the training set, as well as the design of the algorithms. In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification. To this end, we introduce the most comprehensive, large-scale dataset of facial images and videos to date. It consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The data are manually annotated in terms of identity, exact age, gender and kinship. The performance of state-of-the-art models is scrutinized and demographic bias is exposed by conducting a series of experiments. Lastly, a method to debias network embeddings is introduced and tested on the proposed benchmarks.

基于深度学习的方法已经突破了人脸分析技术的极限。然而，尽管这些模型取得了成功，但它们对某些人群的偏见引起了人们的关注。这种偏见既是由于训练集中各个人口统计数据之间有限的多样性，也由于算法的设计而造成的。在这项工作中，我们研究了深度学习模型在人脸识别，年龄估计，性别识别和亲属关系验证方面的人口统计学偏差。为此，我们介绍了迄今为止最全面，最全面的面部图像和视频数据集。它包括在不受约束的真实条件下从1,045个对象中捕获的40K静态图像和44K序列（总共14.5M视频帧）。数据以身份，确切年龄，性别和亲属关系手动注释。通过进行一系列实验来检查最新模型的性能，并揭露人口统计学偏差。最后，介绍了一种消除网络嵌入偏差的方法，并在建议的基准上进行了测试。