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Remote photoplethysmography (rPPG) based physiological measurement has greatapplication values in affective computing, non-contact health monitoring,telehealth monitoring, etc, which has become increasingly important especiallyduring the COVID-19 pandemic. Existing methods are generally divided into twogroups. The first focuses on mining the subtle blood volume pulse (BVP) signalsfrom face videos, but seldom explicitly models the noises that dominate facevideo content. They are susceptible to the noises and may suffer from poorgeneralization ability in unseen scenarios. The second focuses on modelingnoisy data directly, resulting in suboptimal performance due to the lack ofregularity of these severe random noises. In this paper, we propose aDecomposition and Reconstruction Network (DRNet) focusing on the modeling ofphysiological features rather than noisy data. A novel cycle loss is proposedto constrain the periodicity of physiological information. Besides, aplug-and-play Spatial Attention Block (SAB) is proposed to enhance featuresalong with the spatial location information. Furthermore, an efficient PatchCropping (PC) augmentation strategy is proposed to synthesize augmented sampleswith different noise and features. Extensive experiments on different publicdatasets as well as the cross-database testing demonstrate the effectiveness ofour approach.

DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement