论文标题
研究深度分析中的偏见：Kanface数据集和实证研究
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
论文作者
论文摘要
基于深度学习的方法已在面部分析中推动了最先进的方法。然而，尽管它们取得了成功，但这些模型还是对它们对某些人口统计的偏见引起了人们的关注。在训练集中的人口统计学以及算法的设计中，这种偏见既受人口统计学的有限多样性造成的偏见。在这项工作中，我们研究了深度学习模型在面部识别，年龄估计，性别识别和亲属关系验证中的人口偏见。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。它由40k静止图像和44K序列（总共14.50万帧）组成，该序列在1,045名受试者的无约束的现实情况下捕获。数据是根据身份，确切的年龄，性别和亲属关系手动注释的。仔细检查了最新模型的性能，并通过进行一系列实验来暴露人口偏见。最后，在提出的基准测试中引入并测试了Debias网络嵌入的方法。
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.
