  The ability to reliably estimate physiological signals from video is a
powerful tool in low-cost, pre-clinical health monitoring. In this work we
propose a new approach to remote photoplethysmography (rPPG) - the measurement
of blood volume changes from observations of a person's face or skin. Similar
to current state-of-the-art methods for rPPG, we apply neural networks to learn
deep representations with invariance to nuisance image variation. In contrast
to such methods, we employ a fully self-supervised training approach, which has
no reliance on expensive ground truth physiological training data. Our proposed
method uses contrastive learning with a weak prior over the frequency and
temporal smoothness of the target signal of interest. We evaluate our approach
on four rPPG datasets, showing that comparable or better results can be
achieved compared to recent supervised deep learning methods but without using
any annotation. In addition, we incorporate a learned saliency resampling
module into both our unsupervised approach and supervised baseline. We show
that by allowing the model to learn where to sample the input image, we can
reduce the need for hand-engineered features while providing some
interpretability into the model's behavior and possible failure modes. We
release code for our complete training and evaluation pipeline to encourage
reproducible progress in this exciting new direction.
