Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
This work addresses bias in face analysis models, which is a critical issue for fairness in AI applications, but it is incremental as it builds on existing datasets and methods.
The paper tackled bias in deep face analysis by introducing the KANFace dataset, the most comprehensive large-scale dataset with 40K images and 44K video sequences, and exposed demographic bias in state-of-the-art models across tasks like face recognition and age estimation, while also proposing a debiasing method.
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.