Robust Adaptive Heart-Rate Monitoring Using Face Videos

Abstract

Heart rate (HR) monitoring is indispensable for several real-world scenarios, especially when acquired in a non-contact manner. It can be accomplished using face videos acquired from ubiquitous cameras in an inexpensive, non-invasive and unobtrusive manner. But the HR monitoring can be erroneous when the video contains facial expressions, out-of-plane movements, change in camera parameters (like focus) and variations in environmental factors (like illumination). The proposed system mitigates these problems for improving the HR monitoring. For this, it defines an adaptive temporal signal selection mechanism which identifies and removes the facial areas affected by facial expressions. Moreover, it introduces a novel post-processing mechanism which perform HR monitoring by utilizing face reconstruction and quality. The post-processing is used when the face video contains facial movements. Experimental results reveal that incorporation of adaptive temporal signal selection and post-processing mechanisms can significantly improve the HR monitoring. It depicts that the Pearson correlation between actual and estimated HR is 0.95 while the average absolute error is 1.63 beats per minute, which indicates that the proposed system provides good HR monitoring.