A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

TL;DR

This paper compares four methods for remote heart rate estimation from face videos, demonstrating that deep learning approaches significantly outperform handcrafted methods in accuracy, enabling practical applications in medical and sports settings.

Contribution

It provides a comprehensive comparison of existing heart rate estimation methods, highlighting the superior performance of deep learning techniques over handcrafted approaches.

Findings

Deep learning method achieves lower error rates.

Learning-based approach is suitable for real-world applications.

Handcrafted methods are less accurate than deep learning models.

Abstract

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.