PhysMamba: State Space Duality Model for Remote Physiological Measurement
<|reference_start|>PhysMamba: State Space Duality Model for Remote Physiological Measurement: 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 rPPG methods. To address this, we propose PhysMamba, a dual-Pathway time-frequency interaction model via State Space Duality. This method allows the network to learn richer, more representative features, enhancing robustness in noisy conditions. To facilitate information exchange and feature complementation between the two pathways, we design an improved algorithm: Cross-Attention State Space Duality (CASSD). We conduct comparative experiments on the PURE, UBFC-rPPG, and MMPD datasets. Experimental results show that PhysMamba achieves state-of-the-art performance, particularly in complex environments, demonstrating its potential in practical remote physiological signal measurement applications.<|reference_end|>
@article{yan2024physmamba:,
    title={PhysMamba: State Space Duality Model for Remote Physiological
  Measurement},
    author={Zhixin Yan, Yan Zhong, Hongbin Xu, Wenjun Zhang, Lin Shu, Hongbin Xu,
  Wenxiong Kang},
    journal={arXiv preprint arXiv:2408.01077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01077},
    primaryClass={cs.CV}
}