# Spectral Reflectance based Heart Rate Measurement from Facial Video

arXiv: https://arxiv.org/abs/1901.00273 · HTML: https://mlsanity.com/paper/1901.00273/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

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

- [WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning](https://mlsanity.com/paper/2208.05970/) (2022)
- [Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance](https://mlsanity.com/paper/2208.04947/) (2022)
- [Remote Photoplethysmography from Low Resolution videos: An end-to-end solution using Efficient ConvNets](https://mlsanity.com/paper/2208.06817/) (2022)
- [Memristive Computing for Efficient Inference on Resource Constrained Devices](https://mlsanity.com/paper/2208.10490/) (2022)
- [Minute ventilation measurement using Plethysmographic Imaging and lighting parameters](https://mlsanity.com/paper/2208.13319/) (2022)
- [Model Explainability in Physiological and Healthcare-based Neural Networks](https://mlsanity.com/paper/2304.14495/) (2023)

## Abstract (arXiv)

Remote detection of the cardiac pulse has a number of applications in sports and medicine, and can be used to determine the physiological state of the subject. Previous approaches to estimate Heart Rate from video require the subject to remain stationary and employ background information to eliminate illumination interferences. The present research proposes a spectral reflectance based novel illumination rectification method to eliminate illumination variations in the video. Our method does not rely on the background of the video and is robust to extreme motion interferences (head movements). Furthermore, in order to tackle extreme motion artifacts, the present framework introduces a novel feature point recovery system which recovers the feature tracking points lost during extreme head movements of the subject. Finally, the individual HR estimates from multiple feature points are combined to produce an average HR. We evaluate the efficacy of our framework on the MAHNOB HCI dataset, a publicly available dataset employed by previous methods. Our HR measurement framework outperformed previous methods and had a root mean square error of 5.21%.