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 heart rates from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, such as the increase in processing time with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that heart rate information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this thesis for remotely estimating the heart rate under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach exhibits better performance compared with that of the benchmark on the MMSE-HR dataset in terms of both the average heart rate estimation and short-term heart rate estimation. High consistency in short-term heart rate estimation is observed between our method and the ground truth.