  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.
