EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video

TL;DR

This paper introduces EVM-CNN, a real-time, contactless method for estimating heart rate from facial videos using a combination of spatial-temporal filtering and CNN, outperforming benchmarks on the MMSE-HR dataset.

Contribution

The paper presents a novel framework that combines spatial-temporal filtering with CNN for accurate, real-time HR estimation from facial videos under realistic conditions.

Findings

Outperforms benchmark methods on MMSE-HR dataset

Achieves high accuracy in both average and short-time HR estimation

Demonstrates high consistency with ground truth in short-time HR estimation

Abstract

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