  There are demographic biases present in current facial recognition (FR)
models. To measure these biases across different ethnic and gender subgroups,
we introduce our Balanced Faces in the Wild (BFW) dataset. This dataset allows
for the characterization of FR performance per subgroup. We found that relying
on a single score threshold to differentiate between genuine and imposters
sample pairs leads to suboptimal results. Additionally, performance within
subgroups often varies significantly from the global average. Therefore,
specific error rates only hold for populations that match the validation data.
To mitigate imbalanced performances, we propose a novel domain adaptation
learning scheme that uses facial features extracted from state-of-the-art
neural networks. This scheme boosts the average performance and preserves
identity information while removing demographic knowledge. Removing demographic
knowledge prevents potential biases from affecting decision-making and protects
privacy by eliminating demographic information. We explore the proposed method
and demonstrate that subgroup classifiers can no longer learn from features
projected using our domain adaptation scheme. For access to the source code and
data, please visit https://github.com/visionjo/facerec-bias-bfw.
