  Remote photo-plethysmography (rPPG) uses a remotely placed camera to
estimating 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 rate more accurately, but also extracts the pulse waveform to time heart
beats and measure heart rate variability. This method requires no rPPG specific
training and is able to operate in real-time. We validate our method on a
self-recorded dataset under an idealized lab setting, and show state-of-the-art
results on two public dataset with realistic conditions (VicarPPG and PURE).
