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.

翻译：远程光电容积描记术（rPPG）是一种非侵入式技术，旨在捕捉由心脏活动引起的血容量变化所导致的面部像素的细微变化。现有大多数用于rPPG任务的无监督方法侧重于样本间的对比学习，而忽略了生理信号中固有的自相似性先验。本文提出了一种用于无监督rPPG估计的自相似性先验蒸馏（SSPD）框架，该框架利用了心脏活动固有的自相似性。具体而言，我们首先引入一种物理先验嵌入增强技术，以减轻各类噪声的影响。随后，我们定制了一个自相似性感知网络，以提取更可靠的自相似生理特征。最后，我们开发了一种分层自蒸馏范式，以帮助网络从面部视频中解耦出具有自相似性的生理模式。综合实验表明，与最先进的监督方法相比，无监督SSPD框架取得了相当甚至更优的性能。同时，SSPD在端到端模型中保持了最低的推理时间和计算成本。