rPPG-MAE: Self-Supervised Pretraining With Masked Autoencoders for Remote Physiological Measurements

Remote photoplethysmography (rPPG) is an important technique for detecting human vital signs and has received extensive attention. For a long time, researchers have focused attention on supervised methods that rely on large amounts of labeled data. These methods are limited by their need for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on contrastive learning between samples, which neglects inherent self-similar priors in physiological signals and seems to have a limited ability to cope with noise. In this article, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar priors in physiological signals. In addition, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The framework proposed in this article, rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely, PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also outperforms state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset appears to be more important than the size of the dataset used in self-supervised pretraining of the rPPG.