  In response to the ongoing COVID-19 pandemic, we present a robust deep
learning pipeline that is capable of identifying correct and incorrect
mask-wearing from real-time video streams. To accomplish this goal, we devised
two separate approaches and evaluated their performance and run-time
efficiency. The first approach leverages a pre-trained face detector in
combination with a mask-wearing image classifier trained on a large-scale
synthetic dataset. The second approach utilizes a state-of-the-art object
detection network to perform localization and classification of faces in one
shot, fine-tuned on a small set of labeled real-world images. The first
pipeline achieved a test accuracy of 99.97% on the synthetic dataset and
maintained 6 FPS running on video data. The second pipeline achieved a mAP(0.5)
of 89% on real-world images while sustaining 52 FPS on video data. We have
concluded that if a larger dataset with bounding-box labels can be curated,
this task is best suited using object detection architectures such as YOLO and
SSD due to their superior inference speed and satisfactory performance on key
evaluation metrics.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing