Remote photoplethysmography (rPPG) is a non-contact and noninvasive way of measuring human physiological signals such as the heart rate using the subtle color changes of skin regions. Since the face of a person is generally visible, facial videos can be used for estimating the heart rate remotely. The rigid and non-rigid motions of the face and illumination variations are the main challenges that affect the accuracy of heart rate estimation. In this paper, we present a new method for estimating the heart rate of a person from the skin region of the facial video using nonlinear mode decomposition (NMD), which is a recently proposed blind source separation method and has been shown to be more robust to noise. We also propose a new method (history-based consistency check—HBCC) for selecting the best heart rate candidate after decomposition by minimizing a temporal cost function. Experiments on two datasets show that the proposed method (rPPG-NMD) achieves promising results as compared to several the state-of-the-art methods for rPPG-based heart rate estimation.

远程光电容积描记术（rPPG）是一种非接触，无创的方法，可以利用皮肤区域的细微颜色变化来测量人的生理信号（例如心率）。由于人的脸通常是可见的，因此可以使用面部视频远程估计心率。面部的刚性和非刚性运动以及照明变化是影响心率估计准确性的主要挑战。在本文中，我们提出了一种使用非线性模式分解（NMD）来从面部视频的皮肤区域估计人的心率的新方法，该方法是最近提出的盲源分离方法，并且已被证明具有更高的鲁棒性发出噪音。我们还提出了一种新方法（基于历史的一致性检查-HBCC），用于通过最小化时间成本函数来选择分解后的最佳心率候选者。在两个数据集上进行的实验表明，与几种基于rPPG的心率估计的最新方法相比，该方法（rPPG-NMD）取得了可喜的结果。