The non-contact monitoring of vital signs, especially the Heart Rate (HR) and Breathing Rate (BR), using facial video is becoming increasingly important. Although, researchers have made considerable progress in the past few years, there are still some limitations to the technology, such as the lack of challenging datasets, the time consuming nature of the estimation process, and non-portability of the system. In this paper, we proposed a new framework for estimating HRs and BRs by combining a Convolutional Neural Network (CNN) with the Phase-based Video Motion Processing (PVMP) algorithm. The experimental results show that our approach achieves better performance. Meanwhile, we introduce a new challenging dataset with fewer constraints, such as large movements, facial expressions and light interference. In addition, we developed a new Android application, which works in real time and offline, based on a CNN for HR and BR estimations.

使用面部视频对生命体征，尤其是心率 (HR) 和呼吸率 (BR) 进行非接触式监测变得越来越重要。尽管研究人员在过去几年取得了相当大的进步，但该技术仍存在一些局限性，例如缺乏具有挑战性的数据集、估计过程的耗时性以及系统的不可移植性。在本文中，我们通过将卷积神经网络 (CNN) 与基于相位的视频运动处理 (PVMP) 算法相结合，提出了一种用于估计 HR 和 BR 的新框架。实验结果表明，我们的方法取得了更好的性能。同时，我们引入了一个新的具有挑战性的数据集，其约束较少，例如大动作、面部表情和光干扰。此外，