@inproceedings{
wang2024rppghibahierarchical,
title={r{PPG}-HiBa:Hierarchical Balanced Framework for Remote Physiological Measurement},
author={Yin Wang and Hao LU and Ying-Cong Chen and Li Kuang and Mengchu Zhou and Shuiguang Deng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=kckxgbOogx}
}
Remote photoplethysmography (**rPPG**) is a promising technique for non-contact physiological signal measurement, which has great potential in health monitoring and emotion analysis. However, existing methods for the rPPG task ignore the long-tail phenomenon of physiological signal data, especially on multiple domains joint training. In addition, we find that the long-tail problem of the physiological label (phys-label) exists in different datasets, and the long-tail problem of domain exists under the same phys-label. To tackle these problems, in this paper, we propose a **Hi**erarchical **Ba**lanced framework (rPPG-HiBa), which mitigates the bias caused by domain and phys-label imbalance. Specifically, we propose anti-spurious domain center learning tailored to learning domain-balanced embeddings space.  Then, we adopt compact-aware continuity regularization to estimate phys-label-wise imbalances and construct continuity between embeddings. Extensive experiments demonstrate that our method outperforms the state-of-the-art in cross-dataset and intra-dataset settings.
rPPG-HiBa:Hierarchical Balanced Framework for Remote Physiological Measurement
Yin Wang Hao LU Ying-Cong Chen Li Kuang Mengchu Zhou Shuiguang Deng