Analysis of minimum face video duration and the effect of video compression to image-based non-contact heart rate monitoring system

Heart rate (HR) is one of important indicator for human physiological diagnosis, and camera can be used to detect it via photoplethysmograph (PPG) signal extraction. In doing so, number of sample images required to measure the HR signal, and quality of the images itself are important to yield an accurate reading. This paper tackles such an issue by analyzing the effect of sampling interval to HR reading in compressed and original video format, obtained in various ranging locations. Technically, important facial points from video stream were estimated by using cascade regression facial tracker. Based on the facial points, region of interest (ROI) was constructed where non-rigid movement is minimal. Next, PPG signal was extracted by calculating the average value of green pixel intensity from the ROI. Following that, illumination variation was separated from the signal via independent component analysis (ICA). The PPG signal was further processed using series of signal filtering techniques to exclude frequencies beyond range of interest prior estimate the HR. From the experiment it can be observed that sampling time of 2 seconds in uncompressed video shows promising HR within the range of 1 to 5 meters.