Accurate Heart Rate Measuring from Face Video Using Efficient Pixel Selection and Tracking

As the coronavirus (COVID-19) spreads around the world, we are increasingly cognizant of our health on a daily basis. This paper focuses on heart rate monitoring, utilizing remote monitoring methodology as a vital indicator of health status. Remote photoplethysmography (rPPG), is a wellknown technique in human remote monitoring, to calculate the heart rate from face videos. Since rPPG analyzes small changes in: color and motion, physical factors (e.g., breathing and adjusting posture), and environmental factors (e.g., illumination and shade), it is difficult to measure heart rate with precision. To resolve these challenges, this paper proposes a system that effectively combines the following methods: 1) Lucas-Kanade method to dynamically track each skin pixel, 2) selection of proper pixels that are not affected by the environmental fluctuations inlight and shade, 3) the delineation of the heart rate signal from noisy to precise data to improve accuracy, and 4) Fast Fourier Transform (FFT) to estimate the main frequency of the signal to determine the heart rate. The results of the experiment showed that the mean absolute error (MAE) of the heart rate was 3.4 bpm for 72 face videos.