Learning Spatio-Temporal Pulse Representation With Global-Local Interaction and Supervision for Remote Prediction of Heart Rate.

Recent studies have demonstrated the benefit of extracting and fusing pulse signals from multi-scale region-of-interests (ROIs). However, these methods suffer from heavy computational load. This paper aims to effectively utilize multi-scale rPPG features with a more compact architecture. Inspired by recent research works exploring two-path architecture that leverages global and local information with bidirectional bridge in between. This paper designs a novel architecture Global-Local Interaction and Supervision Network (GLISNet), which uses a local path to learn representations in the original scale and a global path to learn representations in the other scale capturing multi-scale information. A light-weight rPPG signal generation block is attached to the output of each path that maps the pulse representation to the pulse output. A hybrid loss function is utilized enabling the local and global representations to learn directly from the training data. Extensive experiments are conducted on two publicly available datasets, and results demonstrate that GLISNet achieves superior performance in terms of signal-to-noise ratio (SNR), mean absolute error (MAE), and root mean squared error (RMSE). In terms of SNR, GLISNet has an increase of 4.41% compared with the second best algorithm PhysNet on PURE dataset. The MAE has a decrease of 13.16% compared with the second best algorithm DeeprPPG on UBFC-rPPG dataset. The RMSE has a decrease of 26.29% compared with the second best algorithm PhysNet on UBFC-rPPG dataset. Experiments on MIHR dataset demonstrates the robustness of GLISNet under low-light environment.