  There are large individual differences in physiological processes, making
designing personalized health sensing algorithms challenging. Existing machine
learning systems struggle to generalize well to unseen subjects or contexts and
can often contain problematic biases. Video-based physiological measurement is
not an exception. Therefore, learning personalized or customized models from a
small number of unlabeled samples is very attractive as it would allow fast
calibrations to improve generalization and help correct biases. In this paper,
we present a novel meta-learning approach called MetaPhys for personalized
video-based cardiac measurement for contactless pulse and heart rate
monitoring. Our method uses only 18-seconds of video for customization and
works effectively in both supervised and unsupervised manners. We evaluate our
proposed approach on two benchmark datasets and demonstrate superior
performance in cross-dataset evaluation with substantial reductions (42% to
44%) in errors compared with state-of-the-art approaches. We have also
demonstrated our proposed method significantly helps reduce the bias in skin
type.
