  Camera-based remote photoplethysmography (rPPG) provides a non-contact way to
measure physiological signals (e.g., heart rate) using facial videos. Recent
deep learning architectures have improved the accuracy of such physiological
measurement significantly, yet they are restricted by the diversity of the
annotated videos. The existing datasets MMSE-HR, AFRL, and UBFC-RPPG contain
roughly 10%, 0%, and 5% of dark-skinned subjects respectively. The unbalanced
training sets result in a poor generalization capability to unseen subjects and
lead to unwanted bias toward different demographic groups. In Western academia,
it is regrettably difficult in a university setting to collect data on these
dark-skinned subjects. Here we show a first attempt to overcome the lack of
dark-skinned subjects by synthetic augmentation. A joint optimization framework
is utilized to translate real videos from light-skinned subjects to dark skin
tones while retaining their pulsatile signals. In the experiment, our method
exhibits around 31% reduction in mean absolute error for the dark-skinned group
and 46% improvement on bias mitigation for all the groups, as compared with the
previous work trained with just real samples.
