Assessment of Deep Learning-Based Heart Rate Estimation Using Remote Photoplethysmography Under Different Illuminations

Remote photoplethysmography (rPPG) monitors heart rate (HR) without requiring physical contact, which has applications. Deep learning based rPPG has demonstrated superior performance over the traditional approaches in controlled context. However, the lighting situation in indoor space is typically complex, with uneven light distribution and frequent variations in illumination. It lacks a fair comparison of different methods under different illuminations using the same dataset. In this article, we present a public dataset, namely the BeiHang University remote photoplethysmography (BH-rPPG) dataset, which contains data from 35 subjects under three illuminations: 1) low; 2) medium; and 3) high illumination. We also provide the ground truth HR measured by an oximeter. We evaluate the performance of three deep learning-based methods (Deepphys, rPPGNet, and Physnet) to that of four traditional methods (CHROM, GREEN, ICA, and POS) using two public datasets: 1) UBFC-rPPG; 2) the BH-rPPG. The experimental results demonstrate that traditional methods are more resistant to fluctuating illuminations. We found that the Physnet achieves lowest mean absolute error among deep learning based method under medium illumination, whereas the CHROM achieves 1.04 beats per minute, outperforming the Physnet by 80
$\%$
. Additionally, we investigate potential methods for improving performance of deep learning based methods. We find that brightness augmentation make model more robust to variation illumination. These findings suggest that while developing deep learning based HR estimation algorithms, illumination variation should be taken into account. This work serves as a benchmark for rPPG performance evaluation and it opens a pathway for future investigation into deep learning based rPPG under illumination variations.