PhysFormer and PhysFormer++, two end-to-end video transformer architectures, demonstrated superior performance for remote photoplethysmography on four benchmark datasets without large-scale pretraining.
PhysFormer and PhysFormer++ provide a novel, high-performing transformer baseline for remote photoplethysmography that can be trained from scratch without large-scale pretraining.
Abstract Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose two end-to-end video transformer based architectures, namely PhysFormer and PhysFormer++, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. To better exploit the temporal contextual and periodic rPPG clues, we also extend the PhysFormer to the two-pathway SlowFast based PhysFormer++ with temporal difference periodic and cross-attention transformers. Furthermore, we propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and PhysFormer++ and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. Unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer family can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community.
Yu et al. (Wed,) conducted a other in Remote photoplethysmography (rPPG). PhysFormer and PhysFormer++ vs. Convolutional neural networks and other transformer networks was evaluated on Performance on intra- and cross-dataset testings. PhysFormer and PhysFormer++, two end-to-end video transformer architectures, demonstrated superior performance for remote photoplethysmography on four benchmark datasets without large-scale pretraining.