TransPhys: Transformer-based unsupervised contrastive learning for remote heart rate measurement

Abstract

Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method based on facial video. Because the rPPG signal is highly susceptible to interference from external conditions such as light changes and head movements, the rPPG signal extracted in natural scenes has a low signal to noise ratio and cannot accurately calculate the heart rate value. Aiming at these problems, this paper constructs a self-supervised machine learning network based on Transformer, TransPhys, to achieve robust measurement of heart rate. Using the self-supervised method of contrastive learning, first perform data augmentation, input the enhanced frame into Stem, and extract coarse local spatial features. Through Spatial-Temporal Transformer, calculate the pulse signal of the face area, and finally output the PPG signal. Compared with other supervised machine learning methods, this method can be trained and achieve ideal results without using any labels, which means that this method can be well generalized to practical applications. The experimental results show that this method is better than the current mainstream self-supervised methods.