  This work summarizes the 2020 ChaLearn Looking at People Fair Face
Recognition and Analysis Challenge and provides a description of the
top-winning solutions and analysis of the results. The aim of the challenge was
to evaluate accuracy and bias in gender and skin colour of submitted algorithms
on the task of 1:1 face verification in the presence of other confounding
attributes. Participants were evaluated using an in-the-wild dataset based on
reannotated IJB-C, further enriched by 12.5K new images and additional labels.
The dataset is not balanced, which simulates a real world scenario where
AI-based models supposed to present fair outcomes are trained and evaluated on
imbalanced data. The challenge attracted 151 participants, who made more than
1.8K submissions in total. The final phase of the challenge attracted 36 active
teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in
the proposed bias metrics. Common strategies by the participants were face
pre-processing, homogenization of data distributions, the use of bias aware
loss functions and ensemble models. The analysis of top-10 teams shows higher
false positive rates (and lower false negative rates) for females with dark
skin tone as well as the potential of eyeglasses and young age to increase the
false positive rates too.
