Eta-Rppgnet: Effective Time-Domain Attention Network For Remote Heart Rate Measurement

Remote photoplethysmography (rPPG) technology is widely used to measure heart rate (HR) from facial video. However, the accuracy of rPPG signal extraction is affected by the slow content changes in long-range facial videos, and the extraction process is also susceptible to interference from illumination variation and head movement noise. To address the above problems, in this article, we propose an end-to-end effective time-domain attention network (ETA-rPPGNet). First, in order to overcome video redundancy information, we construct the time-domain segment suhnet. The video is divided into several segments, which are fed into the subspace networks to extract important spatial facial features and aggregate temporal information, respectively. Then, the time-domain attention mechanism is designed in the backbone net. In this mechanism, the 1-D convolution is used to effectively model the information association in the local time domain, so as to improve the antinoise ability of the model. Finally, via the two-part loss function, the model can reduce the interference of other physiological signals. We conduct extensive experiments to verify the effectiveness of our model on the public PURE, COHFACE, UBFC-rPPG, and MMSE-HR data sets. Compared with other models, our model shows better performance in the measurement accuracy.