  Remote camera measurement of the blood volume pulse via photoplethysmography
(rPPG) is a compelling technology for scalable, low-cost, and accessible
assessment of cardiovascular information. Neural networks currently provide the
state-of-the-art for this task and supervised training or fine-tuning is an
important step in creating these models. However, most current models are
trained on facial videos using contact PPG measurements from the fingertip as
targets/ labels. One of the reasons for this is that few public datasets to
date have incorporated contact PPG measurements from the face. Yet there is
copious evidence that the PPG signals at different sites on the body have very
different morphological features. Is training a facial video rPPG model using
contact measurements from another site on the body suboptimal? Using a recently
released unique dataset with synchronized contact PPG and video measurements
from both the hand and face, we can provide precise and quantitative answers to
this question. We obtain up to 40 % lower mean squared errors between the
waveforms of the predicted and the ground truth PPG signals using
state-of-the-art neural models when using PPG signals from the forehead
compared to using PPG signals from the fingertip. We also show qualitatively
that the neural models learn to predict the morphology of the ground truth PPG
signal better when trained on the forehead PPG signals. However, while models
trained from the forehead PPG produce a more faithful waveform, models trained
from a finger PPG do still learn the dominant frequency (i.e., the heart rate)
well.
