  Remote photo-plethysmography (rPPG) uses a camera to estimate a person's
heart rate (HR). Similar to how heart rate can provide useful information about
a person's vital signs, insights about the underlying physio/psychological
conditions can be obtained from heart rate variability (HRV). HRV is a measure
of the fine fluctuations in the intervals between heart beats. However, this
measure requires temporally locating heart beats with a high degree of
precision. We introduce a refined and efficient real-time rPPG pipeline with
novel filtering and motion suppression that not only estimates heart rates, but
also extracts the pulse waveform to time heart beats and measure heart rate
variability. This unsupervised method requires no rPPG specific training and is
able to operate in real-time. We also introduce a new multi-modal video
dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR
and HRV estimation. We validate and study our method under various conditions
on a comprehensive range of public and self-recorded datasets, showing
state-of-the-art results and providing useful insights into some unique
aspects. Lastly, we make available CleanerPPG, a collection of human-verified
ground truth peak/heart-beat annotations for existing rPPG datasets. These
verified annotations should make future evaluations and benchmarking of rPPG
algorithms more accurate, standardized and fair.
