FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease

TL;DR

FaceTouch is a deep learning framework that detects hand-to-face contact in complex scenes using supervised contrastive learning, aiding in infectious disease transmission tracking.

Contribution

The paper introduces a novel deep learning approach with supervised contrastive learning for detecting hand-to-face touches in real-world scenarios, addressing a gap in existing methods.

Findings

Effective detection of hand-to-face touches in complex urban scenes.

Strong validation on unseen datasets indicating good generalization.

Utilizes body gestures to improve detection despite face occlusion.

Abstract

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.