Conventional and deep learning methods in heart rate estimation from RGB face videos

Contactless vital signs monitoring is a fast-advancing scientific field that aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditional monitoring systems. Several traditional methods have been applied to extract the heart rate (HR) signal from the face. Moreover, machine learning has recently contributed majorly to the development of such a field in which deep networks and other deep learning methods are employed to extract the HR signal from RGB face videos. In this paper, we evaluate the state-of-the-art conventional and deep learning methods for HR estimates, focusing on the limits of deep learning methods and the availability of less-controlled face video datasets. We aim to present an extensive review that helps the various approaches of remote photoplethysmography extraction and HR estimation to be understood, in addition to their drawbacks and benefits.