Deep Learning for Remote Heart Rate Estimation: A Reproducible and Optimal State-of-the-Art Framework

Abstract

Accurate remote pulse rate measurement from RGB face videos has gained a lot of attention in the past years since it allows for a non-invasive contactless monitoring of a subject’s heart rate, useful in numerous potential applications. Nowadays, there is a global trend to monitor e-health parameters without the use of physical devices enabling at-home daily monitoring and telehealth. This paper includes a comprehensive state-of-the-art on remote heart rate estimation from face images. We extensively tested a new framework to better understand several open questions in the domain that are: which areas of the face are the most relevant, how to manage video color components and which performances are possible to reach on a public relevant dataset. From this study, we extract key elements to design an optimal, up-to-date and reproducible framework that can be used as a baseline for accurately estimating the heart rate of a human subject, in particular from the cheek area using the green (G) channel of a RGB video. The results obtained in the public database COHFACE support our input data choices and our 3D-CNN structure as optimal for a remote HR estimation.