Model-based Region of Interest Segmentation for Remote Photoplethysmography

Peixi Li, Yannick Benezeth, Keisuke Nakamura, Randy Gomez, Fan Yang

2019

Abstract

Remote photoplethysmography (rPPG) is a non-contact technique for measuring vital physiological signs, such as heart rate (HR) and respiratory rate (RR). HR is a medical index which is widely used in health monitoring and emotion detection applications. Therefore, HR measurement with rPPG methods offers a convenient and non-invasive method for these applications. The selection of Region Of Interest (ROI) is a critical first step of many rPPG techniques to obtain reliable pulse signals. The ROI should contain as many skin pixels as possible with a minimum of non-skin pixels. Moreover, it has been shown that rPPG signal is not distributed homogeneously on skin. Some skin regions contain more rPPG signal than others, mainly for physiological reasons. In this paper, we propose to explicitly favor areas where the information is more predominant using a spatially weighted average of skin pixels based on a trained model. The proposed method has been compared to several state of the art ROI segmentation methods using a public database, namely the UBFC-RPPG dataset (Bobbia et al., 2017). We have shown that this modification in how the spatial averaging of the ROI pixels is calculated can significantly increase the final performance of heart rate estimate.

Paper Citation

in Harvard Style

Li P., Benezeth Y., Nakamura K., Gomez R. and Yang F. (2019). Model-based Region of Interest Segmentation for Remote Photoplethysmography. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 383-388. DOI: 10.5220/0007389803830388

in Bibtex Style

@conference{visapp19,
author={Peixi Li and Yannick Benezeth and Keisuke Nakamura and Randy Gomez and Fan Yang},
title={Model-based Region of Interest Segmentation for Remote Photoplethysmography},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={383-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007389803830388},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Model-based Region of Interest Segmentation for Remote Photoplethysmography
SN - 978-989-758-354-4
AU - Li P.
AU - Benezeth Y.
AU - Nakamura K.
AU - Gomez R.
AU - Yang F.
PY - 2019
SP - 383
EP - 388
DO - 10.5220/0007389803830388
PB - SciTePress