# A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

arXiv: https://arxiv.org/abs/2005.11101 · HTML: https://mlsanity.com/paper/2005.11101/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 6 of 6)

- [ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning](https://mlsanity.com/paper/2112.09165/) (2021)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)
- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [Your smartphone could act as a pulse-oximeter and as a single-lead ECG](https://mlsanity.com/paper/2305.12583/) (2023)
- [PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics](https://mlsanity.com/paper/2310.02140/) (2023)

## Abstract (arXiv)

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.