  The COVID-19 pandemic raises the problem of adapting face recognition systems
to the new reality, where people may wear surgical masks to cover their noses
and mouths. Traditional data sets (e.g., CelebA, CASIA-WebFace) used for
training these systems were released before the pandemic, so they now seem
unsuited due to the lack of examples of people wearing masks. We propose a
method for enhancing data sets containing faces without masks by creating
synthetic masks and overlaying them on faces in the original images. Our method
relies on SparkAR Studio, a developer program made by Facebook that is used to
create Instagram face filters. In our approach, we use 9 masks of different
colors, shapes and fabrics. We employ our method to generate a number of
445,446 (90%) samples of masks for the CASIA-WebFace data set and 196,254
(96.8%) masks for the CelebA data set, releasing the mask images at
https://github.com/securifai/masked_faces. We show that our method produces
significantly more realistic training examples of masks overlaid on faces by
asking volunteers to qualitatively compare it to other methods or data sets
designed for the same task. We also demonstrate the usefulness of our method by
evaluating state-of-the-art face recognition systems (FaceNet, VGG-face,
ArcFace) trained on our enhanced data sets and showing that they outperform
equivalent systems trained on original data sets (containing faces without
masks) or competing data sets (containing masks generated by related methods),
when the test benchmarks contain masked faces.
