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In recent studies on non-invasive techniques for heart rate measurements, various Computer Vision algorithms based on Ballistocardiography (BCG) have been employed. This method captures minimal head motions from facial videos, that result from the pumping of blood to the head through the carotid arteries, at each cardiac cycle. We move towards BCG because the conventional technique of Photoplethysmography (PPG) fails to yield accurate results from facial video in case of skin color variations. This paper proposes an improved system for accurately measuring the heart rate and heart rate variability to infer important information about the subject’s health. It incorporates functions from the Dlib toolkit, which provide robust face detection along with facial landmark tracking. Relevant data in terms of facial video and ground truth was acquired from 5 test subjects, in 3 states - sitting, standing and post exercise. The system exhibits promising results when validated using a wearable smart watch with inbuilt heart rate sensor.

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