  Remote physiological measurements, e.g., remote photoplethysmography (rPPG)
based heart rate (HR), heart rate variability (HRV) and respiration frequency
(RF) measuring, are playing more and more important roles under the application
scenarios where contact measurement is inconvenient or impossible. Since the
amplitude of the physiological signals is very small, they can be easily
affected by head movements, lighting conditions, and sensor diversities. To
address these challenges, we propose a cross-verified feature disentangling
strategy to disentangle the physiological features with non-physiological
representations, and then use the distilled physiological features for robust
multi-task physiological measurements. We first transform the input face videos
into a multi-scale spatial-temporal map (MSTmap), which can suppress the
irrelevant background and noise features while retaining most of the temporal
characteristics of the periodic physiological signals. Then we take pairwise
MSTmaps as inputs to an autoencoder architecture with two encoders (one for
physiological signals and the other for non-physiological information) and use
a cross-verified scheme to obtain physiological features disentangled with the
non-physiological features. The disentangled features are finally used for the
joint prediction of multiple physiological signals like average HR values and
rPPG signals. Comprehensive experiments on different large-scale public
datasets of multiple physiological measurement tasks as well as the
cross-database testing demonstrate the robustness of our approach.
