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

arXiv: https://arxiv.org/abs/2212.13843 · HTML: https://mlsanity.com/paper/2212.13843/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 25 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)

## Built on by (most cited first, 10 of 10)

- [PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer](https://mlsanity.com/paper/2302.03548/) (2023)
- [rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement](https://mlsanity.com/paper/2306.02301/) (2023)
- [Neuron Structure Modeling for Generalizable Remote Physiological Measurement](https://mlsanity.com/paper/2303.05955/) (2023)
- [Bi-TTA: Bidirectional Test-Time Adapter for Remote Physiological Measurement](https://mlsanity.com/paper/2409.17316/) (2024)
- [Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement](https://mlsanity.com/paper/2311.05100/) (2023)
- [Toward Motion Robustness: A masked attention regularization framework in remote photoplethysmography](https://mlsanity.com/paper/2407.06653/) (2024)
- [PhysMamba: State Space Duality Model for Remote Physiological Measurement](https://mlsanity.com/paper/2408.01077/) (2024)
- [Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement](https://mlsanity.com/paper/2407.08507/) (2024)
- [Non-Contact Physiological Monitoring in Pediatric Intensive Care Units via Adaptive Masking and Self-Supervised Learning](https://mlsanity.com/paper/2602.15967/) (2026)
- [VideoPulse: Neonatal heart rate and peripheral capillary oxygen saturation (SpO2) estimation from contact free video](https://mlsanity.com/paper/2602.23771/) (2026)

## Abstract (arXiv)

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.