  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%.
