A Comparative Survey of Methods for Remote Heart Rate Detection From Frontal Face Videos

Frontiers in Bioengineering and Biotechnology
Chen WangGuillaume Chanel

Abstract

Remotely measuring physiological activity can provide substantial benefits for both the medical and the affective computing applications. Recent research has proposed different methodologies for the unobtrusive detection of heart rate (HR) using human face recordings. These methods are based on subtle color changes or motions of the face due to cardiovascular activities, which are invisible to human eyes but can be captured by digital cameras. Several approaches have been proposed such as signal processing and machine learning. However, these methods are compared with different datasets, and there is consequently no consensus on method performance. In this article, we describe and evaluate several methods defined in literature, from 2008 until present day, for the remote detection of HR using human face recordings. The general HR processing pipeline is divided into three stages: face video processing, face blood volume pulse (BVP) signal extraction, and HR computation. Approaches presented in the paper are classified and grouped according to each stage. At each stage, algorithms are analyzed and compared based on their performance using the public database MAHNOB-HCI. Results found in this article are limited on MAHNOB-HCI data...Continue Reading

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Citations

Jan 30, 2020·Animals : an Open Access Journal From MDPI·Pengfei WangJianqi Wang
Jun 27, 2020·Personalized Medicine·Travis J Cohoon, Sanjeev P Bhavnani
Jun 25, 2021·Resuscitation·Thomas ReaMickey Eisenberg

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Methods Mentioned

BETA
PCA

Software Mentioned

Pulse Meter
Cardiio
MATLAB
MAHNOB
Intraface
Emotional feAture extraction ( TEAP )
OpenFace

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