  Remote photoplethysmography (rPPG) is a noninvasive technique that aims to
capture subtle variations in facial pixels caused by changes in blood volume
resulting from cardiac activities. Most existing unsupervised methods for rPPG
tasks focus on the contrastive learning between samples while neglecting the
inherent self-similar prior in physiological signals. In this paper, we propose
a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG
estimation, which capitalizes on the intrinsic self-similarity of cardiac
activities. Specifically, we first introduce a physical-prior embedded
augmentation technique to mitigate the effect of various types of noise. Then,
we tailor a self-similarity-aware network to extract more reliable self-similar
physiological features. Finally, we develop a hierarchical self-distillation
paradigm to assist the network in disentangling self-similar physiological
patterns from facial videos. Comprehensive experiments demonstrate that the
unsupervised SSPD framework achieves comparable or even superior performance
compared to the state-of-the-art supervised methods. Meanwhile, SSPD maintains
the lowest inference time and computation cost among end-to-end models. The
source codes are available at https://github.com/LinXi1C/SSPD.
