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A non-invasive yet inexpensive method for heart rate (HR) monitoring is ofgreat importance in many real-world applications including healthcare,psychology understanding, affective computing and biometrics. Face videos arecurrently utilized for such HR monitoring, but unfortunately this can lead toerrors due to the noise introduced by facial expressions, out-of-planemovements, camera parameters (like focus change) and environmental factors. Wealleviate these issues by proposing a novel face video based HR monitoringmethod MOMBAT, that is, MOnitoring using Modeling and BAyesian Tracking. Weutilize out-of-plane face movements to define a novel quality estimationmechanism. Subsequently, we introduce a Fourier basis based modeling toreconstruct the cardiovascular pulse signal at the locations containing thepoor quality, that is, the locations affected by out-of-plane face movements.Furthermore, we design a Bayesian decision theory based HR tracking mechanismto rectify the spurious HR estimates. Experimental results reveal that ourproposed method, MOMBAT outperforms state-of-the-art HR monitoring methods andperforms HR monitoring with an average absolute error of 1.329 beats per minuteand the Pearson correlation between estimated and actual heart rate is 0.9746.Moreover, it demonstrates that HR monitoring is significantly

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