The use of facial masks in public spaces has become a social obligation since
the wake of the COVID-19 global pandemic and the identification of facial masks
can be imperative to ensure public safety. Detection of facial masks in video
footages is a challenging task primarily due to the fact that the masks
themselves behave as occlusions to face detection algorithms due to the absence
of facial landmarks in the masked regions. In this work, we propose an approach
for detecting facial masks in videos using deep learning. The proposed
framework capitalizes on the MTCNN face detection model to identify the faces
and their corresponding facial landmarks present in the video frame. These
facial images and cues are then processed by a neoteric classifier that
utilises the MobileNetV2 architecture as an object detector for identifying
masked regions. The proposed framework was tested on a dataset which is a
collection of videos capturing the movement of people in public spaces while
complying with COVID-19 safety protocols. The proposed methodology demonstrated
its effectiveness in detecting facial masks by achieving high precision,
recall, and accuracy.

该研究提出了一种基于深度学习的方法，利用 MTCNN 和 MobileNetV2 算法检测公共场所中戴口罩的人脸，使得公共场所的监督变得更加安全。