# Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance

arXiv: https://arxiv.org/abs/2208.04947 · HTML: https://mlsanity.com/paper/2208.04947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections](https://mlsanity.com/paper/2208.03662/) (2022)
- [Spectral Reflectance based Heart Rate Measurement from Facial Video](https://mlsanity.com/paper/1901.00273/) (2019)

## Abstract (arXiv)

Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues in dealing with motion and illumination artifacts. In this work, we propose a reliable HR estimation framework using the spectral reflectance of the user, which makes it robust to motion and illumination disturbances. We employ deep learning-based frameworks such as Faster RCNNs to perform face detection as opposed to the Viola Jones algorithm employed by previous approaches. We evaluate our method on the MAHNOB HCI dataset and found that the proposed method is able to outperform previous approaches.Estimation of the Heart rate from facial video has a number of applications in the medical and the fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues in dealing with motion and illumination artifacts. In this work, we propose a reliable HR estimation framework using the spectral reflectance of the user, which makes it robust to motion and illumination disturbances. We employ deep learning-based frameworks such as Faster RCNNs to perform face detection as opposed to the Viola-Jones algorithm employed by previous approaches. We evaluate our method on the MAHNOB HCI dataset and found that the proposed method is able to outperform previous approaches.