Spectral Reflectance based Heart Rate Measurement from Facial Video

TL;DR

This paper introduces a spectral reflectance-based method for remote heart rate measurement from facial videos that is robust to motion and illumination variations, eliminating the need for background information.

Contribution

It presents a novel illumination rectification technique and a feature point recovery system to improve HR estimation during extreme head movements.

Findings

Outperforms previous methods with a 5.21% RMS error

Robust to extreme motion and illumination changes

Does not rely on background information

Abstract

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%.