  Remote photoplethysmography (rPPG), which aims at measuring heart activities
without any contact, has great potential in many applications (e.g., remote
healthcare). Existing rPPG approaches rely on analyzing very fine details of
facial videos, which are prone to be affected by video compression. Here we
propose a two-stage, end-to-end method using hidden rPPG information
enhancement and attention networks, which is the first attempt to counter video
compression loss and recover rPPG signals from highly compressed videos. The
method includes two parts: 1) a Spatio-Temporal Video Enhancement Network
(STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal
recovery. The rPPGNet can work on its own for robust rPPG measurement, and the
STVEN network can be added and jointly trained to further boost the performance
especially on highly compressed videos. Comprehensive experiments are performed
on two benchmark datasets to show that, 1) the proposed method not only
achieves superior performance on compressed videos with high-quality videos
pair, 2) it also generalizes well on novel data with only compressed videos
available, which implies the promising potential for real world applications.
