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.

翻译：远程光电容积描记术（rPPG）提供了一种通过分析面部视频来估计人体脉搏的先进非接触式方法。尽管具有潜力，但rPPG方法容易受到各种伪影的影响，例如噪声、遮挡以及由太阳镜、口罩甚至无意识的面部接触（如人们不自觉地触碰面部）造成的其他障碍。在本研究中，我们应用图像处理变换有意降低视频质量，模拟这些具有挑战性的条件，随后评估基于非学习和学习的rPPG方法在劣化数据上的性能。我们的结果显示，这些伪影的存在显著降低了准确性，这促使我们提出应用诸如去噪和修复等恢复技术来改善心率估计结果。通过应对这些具有挑战性的条件和遮挡伪影，我们的方法旨在使rPPG方法更加鲁棒并适应现实世界情况。为了评估所提出方法的有效性，我们在三个公开数据集上进行了全面的实验，涵盖了广泛的场景和伪影类型。我们的研究结果强调了通过采用恢复算法和rPPG技术的最佳组合来构建鲁棒的rPPG系统的潜力。此外，我们的研究有助于推进注重隐私的rPPG方法，从而在现实和多样化的条件下提高这一创新技术在远程心率估计领域的整体效用和影响。