Heart Rate Estimation from RGB Facial Videos Using Robust Face Demarcation and VMD

Abstract

The recent studies suggest the feasibility of accessing crucial health parameters through contactless means with an RGB camera placed at a distance. As high-quality RGB cameras are getting more cost-effective due to the drastic evolution in imaging technology, the camera-based health monitoring is evoking a considerable interest among researchers. This development may provide a better alternative to the conventional contact-based methods, as it promises a convenient and contactless long term vital sign monitoring solution that doesn't restrict personal mobility. This paper introduces an effective approach towards monitoring heart rate (HR) from facial videos using an RGB camera in wild practical scenarios. The proposed approach introduces the face symmetry-based quality scoring, which is an essential step to ensure quality face detection and avoid false face detections in videos captured in a practical scenario. Further, steps such as feature points generation for optimum masking and variational mode decomposition (VMD) based filtering assist in obtaining a signal dominated mainly by the HR component. Two publicly available datasets comprising the video signals at different frame rates collected from the subjects with diverse ethnicities and skin tones are used to access the performance of the technique. The proposed approach achieved a mean absolute error of 6.58 beats per minute (BPM) on the COHFACE (Good illumination) dataset class, 9.11 BPM on the COHFACE (Bad illumination) dataset class and 6.37 BPM on the DEAP dataset class outperforming some of the state-of-art methods affirming its effectiveness in the estimation of HR in more realistic scenarios.