  Remote heart rate estimation is the measurement of heart rate without any
physical contact with the subject and is accomplished using remote
photoplethysmography (rPPG) in this work. rPPG signals are usually collected
using a video camera with a limitation of being sensitive to multiple
contributing factors, e.g. variation in skin tone, lighting condition and
facial structure. End-to-end supervised learning approach performs well when
training data is abundant, covering a distribution that doesn't deviate too
much from the distribution of testing data or during deployment. To cope with
the unforeseeable distributional changes during deployment, we propose a
transductive meta-learner that takes unlabeled samples during testing
(deployment) for a self-supervised weight adjustment (also known as
transductive inference), providing fast adaptation to the distributional
changes. Using this approach, we achieve state-of-the-art performance on
MAHNOB-HCI and UBFC-rPPG.
