论文标题
MOMBAT：使用脉冲建模和贝叶斯跟踪从面部视频监测的心率监测
MOMBAT: Heart Rate Monitoring from Face Video using Pulse Modeling and Bayesian Tracking
论文作者
论文摘要
在许多现实世界中，包括医疗保健，心理学理解，情感计算和生物识别技术在内的许多现实应用程序中，一种无创的但廉价的心率方法（HR）监测方法非常重要。目前，面部视频用于此类人力资源监控，但不幸的是，由于面部表情，平面外运动，摄像头参数（例如焦点更改）和环境因素引起的噪音，这可能导致错误。我们通过提出一种基于面部视频的HR监控方法Mombat来缓解这些问题，即使用建模和贝叶斯跟踪监测。我们利用平面外的面部运动来定义一种新颖的质量估计机制。随后，我们引入了基于傅立叶的基础建模，以重建包含质量差的位置的心血管脉冲信号，即受平面外面部运动影响的位置。此外，我们设计了基于贝叶斯决策理论的HR跟踪机制，以纠正虚假的人力资源估计。实验结果表明，我们提出的方法Mombat优于最先进的HR监测方法，并执行HR监测，平均绝对误差为每分钟1.329 BEATS，估计和实际心率之间的Pearson相关性为0.9746。此外，它表明人力资源监测是显着的
A non-invasive yet inexpensive method for heart rate (HR) monitoring is of great importance in many real-world applications including healthcare, psychology understanding, affective computing and biometrics. Face videos are currently utilized for such HR monitoring, but unfortunately this can lead to errors due to the noise introduced by facial expressions, out-of-plane movements, camera parameters (like focus change) and environmental factors. We alleviate these issues by proposing a novel face video based HR monitoring method MOMBAT, that is, MOnitoring using Modeling and BAyesian Tracking. We utilize out-of-plane face movements to define a novel quality estimation mechanism. Subsequently, we introduce a Fourier basis based modeling to reconstruct the cardiovascular pulse signal at the locations containing the poor quality, that is, the locations affected by out-of-plane face movements. Furthermore, we design a Bayesian decision theory based HR tracking mechanism to rectify the spurious HR estimates. Experimental results reveal that our proposed method, MOMBAT outperforms state-of-the-art HR monitoring methods and performs HR monitoring with an average absolute error of 1.329 beats per minute and the Pearson correlation between estimated and actual heart rate is 0.9746. Moreover, it demonstrates that HR monitoring is significantly
