Anti-jamming heart rate estimation using a spatial–temporal fusion network

摘要

Remote heart rate estimation based on face video has attracted increasing research attention. The previous methods are mostly based on ideal face features, where face videos are obtained under controllable experimental conditions, with uniform lighting and little noise interference. However, there is a big deviation from the real world scene. The lighting changes, head movements, and object occlusion are inevitable in complex scenes, which will lead to the failure of face positioning and feature extraction from video, and the instability of the facial signal required for heart rate estimation. In this paper, an anti-jamming network is proposed to improve the robustness of handling less-constrained scenarios. Specifically, a new spatial–temporal map generation mechanism is designed to enhance the spatial and temporal features representation by equivalent padding for low-quality video frame fragments. Meanwhile, a heart rate estimation stability module is built to evaluate the quality of the face signals and assign reasonable weights to video clips. Our approach significantly outperforms all current state-of-the-art methods on VIPL-HR dataset.