Remote photoplethysmography (rPPG) offers a state-of-the-art, non-contact
methodology for estimating human pulse by analyzing facial videos. Despite its
potential, rPPG methods can be susceptible to various artifacts, such as noise,
occlusions, and other obstructions caused by sunglasses, masks, or even
involuntary facial contact, such as individuals inadvertently touching their
faces. In this study, we apply image processing transformations to
intentionally degrade video quality, mimicking these challenging conditions,
and subsequently evaluate the performance of both non-learning and
learning-based rPPG methods on the deteriorated data. Our results reveal a
significant decrease in accuracy in the presence of these artifacts, prompting
us to propose the application of restoration techniques, such as denoising and
inpainting, to improve heart-rate estimation outcomes. By addressing these
challenging conditions and occlusion artifacts, our approach aims to make rPPG
methods more robust and adaptable to real-world situations. To assess the
effectiveness of our proposed methods, we undertake comprehensive experiments
on three publicly available datasets, encompassing a wide range of scenarios
and artifact types. Our findings underscore the potential to construct a robust
rPPG system by employing an optimal combination of restoration algorithms and
rPPG techniques. Moreover, our study contributes to the advancement of
privacy-conscious rPPG methodologies, thereby bolstering the overall utility
and impact of this innovative technology in the field of remote heart-rate
estimation under realistic and diverse conditions.

该研究利用图像处理技术恶化视频质量，模拟挑战性条件，评估了非学习和基于学习的 rPPG 方法在恶劣数据上的表现。结果显示这些限制条件下的准确率显著下降，因此建议应用去噪和修填等恢复技术来改善心率估计结果并提高方法的鲁棒性和适应性。