  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.
