Remote photoplethysmography (rPPG) based physiological measurement has great application values in affective computing, non-contact health monitoring, telehealth monitoring, etc, which has become increasingly important especially during the COVID-19 pandemic. Existing methods are generally divided into two groups. The first focuses on mining the subtle blood volume pulse (BVP) signals from face videos, but seldom explicitly models the noises that dominate face video content. They are susceptible to the noises and may suffer from poor generalization ability in unseen scenarios. The second focuses on modeling noisy data directly, resulting in suboptimal performance due to the lack of regularity of these severe random noises. In this paper, we propose a Decomposition and Reconstruction Network (DRNet) focusing on the modeling of physiological features rather than noisy data. A novel cycle loss is proposed to constrain the periodicity of physiological information. Besides, a plug-and-play Spatial Attention Block (SAB) is proposed to enhance features along with the spatial location information. Furthermore, an efficient Patch Cropping (PC) augmentation strategy is proposed to synthesize augmented samples with different noise and features. Extensive experiments on different public datasets as well as the cross-database testing demonstrate the effectiveness of our approach.
翻译:远距离光谱成像仪(rPPG)的生理测量在感性计算、非接触健康监测、远程健康监测等方面有着巨大的应用价值,特别是在COVID-19大流行期间,这种应用变得日益重要,特别是在COVID-19大流行期间,这种应用变得日益重要。现有方法一般分为两类:第一种方法侧重于挖掘面部录像的细微血液体积脉冲信号(BVP),但很少明确地模拟以视频内容为主的噪音;它们容易受到噪音的影响,在不可见的场景中可能受到一般化能力差的损害;第二种方法侧重于制作噪音数据模型,直接导致这些严重随机噪音缺乏规律性,造成不优化的性能。在本文件中,我们提议建立一个分解和重建网络(DRNet),侧重于生理特征的建模,而不是扰动数据。提议新的循环损失将限制生理信息的周期性。此外,还提议建立一个插播空间关注区,以加强空间定位信息的特征。此外,还提议一个高效的补裁剪裁(PC)增强战略,目的是用不同噪音和特征合成样品,以不同的特征和特征为交叉测试基础。