Deep learning for remote heart rate estimation: A reproducible and optimal state-of-the-art framework

Mirabet-Herranz, Nélida; Mallat, Khawla; Dugelay, Jean-Luc

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.

Digital Security
Eurecom Ref:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in and is available at :