FaceTouch: Detecting hand-to-face touch with supervised contrastive
  learning to assist in tracing infectious disease
Mohamed R. Ibrahim Terry Lyons
Through our respiratory system, many viruses and diseases frequently spread
and pass from one person to another. Covid-19 served as an example of how
crucial it is to track down and cut back on contacts to stop its spread. There
is a clear gap in finding automatic methods that can detect hand-to-face
contact in complex urban scenes or indoors. In this paper, we introduce a
computer vision framework, called FaceTouch, based on deep learning. It
comprises deep sub-models to detect humans and analyse their actions. FaceTouch
seeks to detect hand-to-face touches in the wild, such as through video chats,
bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced
system learns to detect face touches from the RGB representation of a given
scene by utilising the representation of the body gestures such as arm
movement. This has been demonstrated to be useful in complex urban scenarios
beyond simply identifying hand movement and its closeness to faces. Relying on
Supervised Contrastive Learning, the introduced model is trained on our
collected dataset, given the absence of other benchmark datasets. The framework
shows a strong validation in unseen datasets which opens the door for potential
deployment.