Abstract
Remote photoplethysmography (rPPG) is a known family of techniques for monitoring blood volume changes from a camera. It may be especially useful for widespread contact-less health monitoring when used to analyze face video from consumer-grade visible-light cameras. The COVID-19 pandemic has caused the widespread use of protective face masks to prevent virus transmission. We found that occlusions from face masks affect face video-based rPPG as the mean absolute error of blood volume estimation is nearly doubled when the face is partially occluded by protective masks. To our knowledge, this paper is the first to analyse the impact of face masks on the accuracy of blood volume pulse estimation and offers several novel elements: (a) two publicly available pulse estimation datasets acquired from 86 unmasked and 61 masked subjects, (b) evaluations of handcrafted algorithms and a 3D convolutional neural network trained on videos of full (unmasked) faces and synthetically generated masks, and (c) data augmentation method (a generator adding a synthetic mask to a face video). Our findings help identify how face masks degrade accuracy of face video analysis, and we discuss paths toward more robust pulse estimation in their presence. The datasets and source codes of all proposed methods are available along with this paper.
Abstract (translated)
URL
https://arxiv.org/abs/2101.04096
