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 this https URL

我们提出了一种基于自相似性先验蒸馏 (SSPD) 框架的无监督远程光电容积描记术 (rPPG) 估计方法，通过利用心脏活动的内在自相似性，采用物理先验嵌入增强技术抑制噪音，利用自相似性感知网络提取更可靠的自相似生理特征，以及开发分层自蒸馏范式来帮助网络从面部视频中解开自相似的生理模式。全面的实验证明，无监督的 SSPD 框架在性能上相当或甚至优于最先进的有监督方法，同时 SSPD 在端到端模型中保持最低的推断时间和计算成本。