Measuring Accuracy Towards Facial Video Heart-Rate Estimation Using Haar-Cascade and CNN Method

COVID-19 leads us to have a social distancing even for health-treatment. In this study, we attempt to estimate heart rates in humans using camera-based remote photoplethysmography (rPPG) methods, which are named after conventional PPG methods. The basic concept is focused on capturing minute variations in skin color during the human body's cardiac cycle, which involves the inflow and outflow of blood from the heart to other body parts. We have compared the performance of different methods of Blind Source Separation and face detection which form an integral part in accurately calculating the heart rate. Purpose: The purpose of this method was comparing the actual heart rate with a tuned parameter of Face Video Heart Rate estimation with CNN and OpenCV haar-cascade. Patients and methods: Videos in the dataset are run through a face detection model to get the region of interest for heart rate calculation. Source signals are converted to frequency domain for filtering and peak detection to obtain heart rate estimates Results: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module, which is as expected. Conclusion: Face segmentation using Convolution Neural Network gives better results than the Haar Cascade OpenCV face detection module. CNNs are slower to detect faces than the Open-CV module. Choosing an ROI by segmenting out facial pixels helped to keep the outliers low and therefore increased the robustness.