  Remote Photoplethysmography (rPPG), or the remote monitoring of a subject's
heart rate using a camera, has seen a shift from handcrafted techniques to deep
learning models. While current solutions offer substantial performance gains,
we show that these models tend to learn a bias to pulse wave features inherent
to the training dataset. We develop augmentations to mitigate this learned bias
by expanding both the range and variability of heart rates that the model sees
while training, resulting in improved model convergence when training and
cross-dataset generalization at test time. Through a 3-way cross dataset
analysis we demonstrate a reduction in mean absolute error from over 13 beats
per minute to below 3 beats per minute. We compare our method with other recent
rPPG systems, finding similar performance under a variety of evaluation
parameters.
