Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation

Abstract

Remote photoplethysmography (rPPG) is a contactless method to measure human vital signs by detecting subtle skin color changes through a camera. Although many studies have used region of interest (ROI) selection tools to improve rPPG signal extraction, no study has investigated the influence of the ROI’s surface orientation. We propose a novel ‘angle map’ representation of the face to study the effects of the surface orientation on the extracted rPPG signal. The angle map is generated by mapping each facial pixel to an angle of reflection (angle between the skin surface and the camera) calculated from the surface normal of the facial landmarks and the camera axis. Our results show that surface orientation significantly affects the correlation between the extracted rPPG signal and ground truth blood volume pulse (BVP). Regions with small angles of reflection contained stronger signals, which explains why areas near the cheeks and forehead are often chosen for rPPG signal extraction. Moreover, we applied a thresholding method to the angle map and demonstrated its potential for dynamic ROI selection, thereby optimising the rPPG signal extraction process.

Cite

Text

Wong et al. "Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00235

Markdown

[Wong et al. "Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/wong2022cvprw-optimising/) doi:10.1109/CVPRW56347.2022.00235

BibTeX

@inproceedings{wong2022cvprw-optimising,
  title     = {{Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation}},
  author    = {Wong, Kwan Long and Chin, Jing Wei and Chan, Tsz Tai and Odinaev, Ismoil and Suhartono, Kristian and Tianqu, Kang and So, Richard Hau Yue},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {2164-2170},
  doi       = {10.1109/CVPRW56347.2022.00235},
  url       = {https://mlanthology.org/cvprw/2022/wong2022cvprw-optimising/}
}