  Camera-based physiological monitoring, especially remote photoplethysmography
(rPPG), is a promising tool for health diagnostics, and state-of-the-art pulse
estimators have shown impressive performance on benchmark datasets. We argue
that evaluations of modern solutions may be incomplete, as we uncover failure
cases for videos without a live person, or in the presence of severe noise. We
demonstrate that spatiotemporal deep learning models trained only with live
samples "hallucinate" a genuine-shaped pulse on anomalous and noisy videos,
which may have negative consequences when rPPG models are used by medical
personnel. To address this, we offer: (a) An anomaly detection model, built on
top of the predicted waveforms. We compare models trained in open-set (unknown
abnormal predictions) and closed-set (abnormal predictions known when training)
settings; (b) An anomaly-aware training regime that penalizes the model for
predicting periodic signals from anomalous videos. Extensive experimentation
with eight research datasets (rPPG-specific: DDPM, CDDPM, PURE, UBFC, ARPM;
deep fakes: DFDC; face presentation attack detection: HKBU-MARs; rPPG outlier:
KITTI) show better accuracy of anomaly detection for deep learning models
incorporating the proposed training (75.8%), compared to models trained
regularly (73.7%) and to hand-crafted rPPG methods (52-62%).
