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Remote photoplethysmography (rPPG), which aims at measuring heart activitiesand physiological signals from facial video without any contact, has greatpotential in many applications (e.g., remote healthcare and affectivecomputing). Recent deep learning approaches focus on mining subtle rPPG cluesusing convolutional neural networks with limited spatio-temporal receptivefields, which neglect the long-range spatio-temporal perception and interactionfor rPPG modeling. In this paper, we propose the PhysFormer, an end-to-endvideo transformer based architecture, to adaptively aggregate both local andglobal spatio-temporal features for rPPG representation enhancement. As keymodules in PhysFormer, the temporal difference transformers first enhance thequasi-periodic rPPG features with temporal difference guided global attention,and then refine the local spatio-temporal representation against interference.Furthermore, we also propose the label distribution learning and a curriculumlearning inspired dynamic constraint in frequency domain, which provideelaborate supervisions for PhysFormer and alleviate overfitting. Comprehensiveexperiments are performed on four benchmark datasets to show our superiorperformance on both intra- and cross-dataset testings. One highlight is that,unlike most transformer networks needed pretraining from large-scale datasets,the proposed PhysFormer can be easily trained from scratch on rPPG datasets,which makes it promising as a novel transformer baseline for the rPPGcommunity. The codes will be released athttps://github.com/ZitongYu/PhysFormer.

PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer