  Heart beat rhythm and heart rate (HR) are important physiological parameters
of the human body. This study presents an efficient multi-hierarchical
spatio-temporal convolutional network that can quickly estimate remote
physiological (rPPG) signal and HR from face video clips. First, the facial
color distribution characteristics are extracted using a low-level face feature
Generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal
stack convolution module (STSC) and multi-hierarchical feature fusion module
(MHFF) are used to strengthen the spatio-temporal correlation of multi-channel
features. In the MHFF, sparse optical flow is used to capture the tiny motion
information of faces between frames and generate a self-adaptive region of
interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to
extract the estimated rPPG signal. The experimental results on the three
datasets show that the proposed network outperforms the state-of-the-art
methods.
