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 improved by incorporating the pulse modeling and HR tracking.

在许多实际应用中，包括医疗保健，对心理的了解，情感计算和生物识别技术，一种非侵入性的但廉价的心率（HR）监测方法非常重要。面部视频当前用于此类HR监视，但是不幸的是，由于面部表情，平面外移动，相机参数（如焦点更改）和环境因素所引入的噪声，这可能导致错误。我们通过提出一种基于人脸视频的新型HR监控方法来缓解这些问题$\mathrm{中号}Ø\mathrm{中号}乙\mathrm{一种}Ť$，即使用建模和贝叶斯跟踪进行监控。我们利用平面外人脸移动来定义一种新颖的质量估计机制。随后，我们引入基于傅立叶基础的建模方法，以在质量较差的位置（即受平面外面部移动影响的位置）重建心血管脉冲信号。此外，我们设计了一种基于贝叶斯决策理论的人力资源跟踪机制，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，$\mathrm{中号}Ø\mathrm{中号}乙\mathrm{一种}Ť$胜过最新的HR监测方法，执行HR监测时的平均绝对误差为每分钟1.329次，并且估计心率与实际心率之间的Pearson相关性为0.9746。此外，它表明通过合并脉冲建模和HR跟踪，可以显着改善HR监视。