Quality-based Pulse Estimation from NIR Face Video with Application to Driver Monitoring
Abstract
In this paper we develop a robust for heart rate (HR) estimation method using face video for challenging scenarios with high variability sources such as head movement, illumination changes, vibration, blur, etc. Our method employs a quality measure Q to extract a remote Plethysmography (rPPG) signal as clean as possible from a specific face video segment. Our main motivation is developing robust technology for driver monitoring. Therefore, for our experiments we use a self-collected dataset consisting of Near Infrared (NIR) videos acquired with a camera mounted in the dashboard of a real moving car. We compare the performance of a classic rPPG algorithm, and the performance of the same method, but using Q for selecting which video segments present a lower amount of variability. Our results show that using the video segments with the highest quality in a realistic driving setup improves the HR estimation with a relative accuracy improvement larger than 20%.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2019
- DOI:
- 10.48550/arXiv.1905.06568
- arXiv:
- arXiv:1905.06568
- Bibcode:
- 2019arXiv190506568H
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Electrical Engineering and Systems Science - Image and Video Processing;
- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- Preprint of the paper presented to IbPRIA 2019