With the improvement of sensor technology and significant algorithmic
advances, the accuracy of remote heart rate monitoring technology has been
significantly improved. Despite of the significant algorithmic advances, the
performance of rPPG algorithm can degrade in the long-term, high-intensity
continuous work occurred in evenings or insufficient light environments. One of
the main challenges is that the lost facial details and low contrast cause the
failure of detection and tracking. Also, insufficient lighting in video
capturing hurts the quality of physiological signal. In this paper, we collect
a large-scale dataset that was designed for remote heart rate estimation
recorded with various illumination variations to evaluate the performance of
the rPPG algorithm (Green, ICA, and POS). We also propose a low-light
enhancement solution (technical solution) for remote heart rate estimation
under the low-light condition. Using collected dataset, we found 1) face
detection algorithm cannot detect faces in video captured in low light
conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to
the noise signal to be in the dominant position; and 3) the chrominance-based
method suffers from the limitation in the assumption about skin-tone will not
hold, and Green and ICA method receive less influence than POS in dark
illuminance environment. The proposed solution for rPPG process is effective to
detect and improve the signal-to-noise ratio and precision of the pulsatile
signal.

本文提出了一种基于低光照条件下的远程心率测量的技术性解决方案，并通过一个大规模的数据集，评估了在不同光照变化下 rPPG 算法（Green、ICA 和 POS）的性能，并发现在低光照条件下，人脸检测算法无法检测人脸，脉冲信号的振幅降低将导致噪声信号占主导地位，并且基于色度的方法在皮肤色调假设方面存在局限性，提出的解决方案可以有效地提高脉冲信号的信噪比和精度。