MOMBAT: Heart Rate Monitoring from Face Video using Pulse Modeling and Bayesian Tracking

• URL: http://arxiv.org/abs/2005.04618v1
• Date: Sun, 10 May 2020 09:41:16 GMT
• Title: MOMBAT: Heart Rate Monitoring from Face Video using Pulse Modeling and Bayesian Tracking
• Authors: Puneet Gupta, Brojeshwar Bhowmick, Arpan Pal
• Abstract summary: We propose a novel face video based HR monitoring method MOMBAT. We utilize out-of-plane face movements to define a novel quality estimation mechanism. We design a Bayesian decision theory based HR tracking mechanism to rectify the spurious HR estimates.
• Score: 10.43230025523549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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