Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge

Understand

Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition.

• The demand for rPPG tasks has expanded from demonstrating good performance on intra-dataset testing to cross-dataset testing (i.e., domain generalization).
• However, most existing methods have overlooked the prior knowledge of rPPG, resulting in poor generalization ability.
• In this paper, we propose a novel framework that simultaneously utilizes explicit and implicit prior knowledge in the rPPG task.