Remote photoplethysmography (rPPG) is a non-contact technique for measuring
cardiac signals from facial videos. High-quality rPPG pulse signals are
urgently demanded in many fields, such as health monitoring and emotion
recognition. However, most of the existing rPPG methods can only be used to get
average heart rate (HR) values due to the limitation of inaccurate pulse
signals. In this paper, a new framework based on generative adversarial
network, called PulseGAN, is introduced to generate realistic rPPG pulse
signals through denoising the chrominance signals. Considering that the cardiac
signal is quasi-periodic and has apparent time-frequency characteristics, the
error losses defined in time and spectrum domains are both employed with the
adversarial loss to enforce the model generating accurate pulse waveforms as
its reference. The proposed framework is tested on the public UBFC-RPPG
database in both within-database and cross-database configurations. The results
show that the PulseGAN framework can effectively improve the waveform quality,
thereby enhancing the accuracy of HR, the heart rate variability (HRV) and the
interbeat interval (IBI). The proposed method achieves the best performance
compared to the denoising autoencoder (DAE) and CHROM, with the mean absolute
error of AVNN (the average of all normal-to-normal intervals) improving 20.85%
and 41.19%, and the mean absolute error of SDNN (the standard deviation of all
NN intervals) improving 20.28% and 37.53%, respectively, in the cross-database
test. This framework can be easily extended to other existing deep learning
based rPPG methods, which is expected to expand the application scope of rPPG
techniques.

本文提出了一种新的基于生成对抗神经网络的框架，名为 PulseGAN，用于通过去噪染色信号产生逼真的远程光电容积脉搏测量 (rPPG) 脉搏信号，以提高健康监控和情感识别的准确性。该框架在公共 UBFC-RPPG 数据库中得到了验证，表明 PulseGAN 可以有效地提高波形质量，进而增强心率 (HR)、心率变异性 (HRV) 和心动间隔 (IBI) 的准确性，且显著优于其他方法。