  With the increase in health consciousness, noninvasive body monitoring has
aroused interest among researchers. As one of the most important pieces of
physiological information, researchers have remotely estimated the heart rate
(HR) from facial videos in recent years. Although progress has been made over
the past few years, there are still some limitations, like the processing time
increasing with accuracy and the lack of comprehensive and challenging datasets
for use and comparison. Recently, it was shown that HR information can be
extracted from facial videos by spatial decomposition and temporal filtering.
Inspired by this, a new framework is introduced in this paper to remotely
estimate the HR under realistic conditions by combining spatial and temporal
filtering and a convolutional neural network. Our proposed approach shows
better performance compared with the benchmark on the MMSE-HR dataset in terms
of both the average HR estimation and short-time HR estimation. High
consistency in short-time HR estimation is observed between our method and the
ground truth.
