Quality-based Pulse Estimation from NIR Face Video with Application to Driver Monitoring
This work addresses robust driver monitoring by improving heart rate estimation in real-world moving car scenarios, though it is incremental as it builds on existing rPPG methods.
The paper tackled heart rate estimation from face video in challenging driving conditions by using a quality measure to select video segments with less variability, resulting in a relative accuracy improvement of over 20%.
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%.