  Remote Photoplethysmography (rPPG) is a non-contact technique for extracting
physiological signals from facial videos, used in applications like emotion
monitoring, medical assistance, and anti-face spoofing. Unlike controlled
laboratory settings, real-world environments often contain motion artifacts and
noise, affecting the performance of existing methods. To address this, we
propose PhysMamba, a dual-stream time-frequency interactive model based on
Mamba. PhysMamba integrates the state-of-the-art Mamba-2 model and employs a
dual-stream architecture to learn diverse rPPG features, enhancing robustness
in noisy conditions. Additionally, we designed the Cross-Attention State Space
Duality (CASSD) module to improve information exchange and feature
complementarity between the two streams. We validated PhysMamba using PURE,
UBFC-rPPG and MMPD. Experimental results show that PhysMamba achieves
state-of-the-art performance across various scenarios, particularly in complex
environments, demonstrating its potential in practical remote heart rate
monitoring applications.
