  The wearing of the face masks appears as a solution for limiting the spread
of COVID-19. In this context, efficient recognition systems are expected for
checking that people faces are masked in regulated areas. To perform this task,
a large dataset of masked faces is necessary for training deep learning models
towards detecting people wearing masks and those not wearing masks. Some large
datasets of masked faces are available in the literature. However, at the
moment, there are no available large dataset of masked face images that permits
to check if detected masked faces are correctly worn or not. Indeed, many
people are not correctly wearing their masks due to bad practices, bad
behaviors or vulnerability of individuals (e.g., children, old people). For
these reasons, several mask wearing campaigns intend to sensitize people about
this problem and good practices. In this sense, this work proposes three types
of masked face detection dataset; namely, the Correctly Masked Face Dataset
(CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for
the global masked face detection (MaskedFace-Net). Realistic masked face
datasets are proposed with a twofold objective: i) to detect people having
their faces masked or not masked, ii) to detect faces having their masks
correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To
the best of our knowledge, no large dataset of masked faces provides such a
granularity of classification towards permitting mask wearing analysis.
Moreover, this work globally presents the applied mask-to-face deformable model
for permitting the generation of other masked face images, notably with
specific masks. Our datasets of masked face images (137,016 images) are
available at https://github.com/cabani/MaskedFace-Net.
