  In December 2019, a novel coronavirus (COVID-19) spread so quickly around the
world that many countries had to set mandatory face mask rules in public areas
to reduce the transmission of the virus. To monitor public adherence,
researchers aimed to rapidly develop efficient systems that can detect faces
with masks automatically. However, the lack of representative and novel
datasets proved to be the biggest challenge. Early attempts to collect face
mask datasets did not account for potential race, gender, and age biases.
Therefore, the resulting models show inherent biases toward specific race
groups, such as Asian or Caucasian. In this work, we present a novel face mask
detection dataset that contains images posted on Twitter during the pandemic
from around the world. Unlike previous datasets, the proposed Bias-Aware Face
Mask Detection (BAFMD) dataset contains more images from underrepresented race
and age groups to mitigate the problem for the face mask detection task. We
perform experiments to investigate potential biases in widely used face mask
detection datasets and illustrate that the BAFMD dataset yields models with
better performance and generalization ability. The dataset is publicly
available at https://github.com/Alpkant/BAFMD.
