Model-based (passive) heart rate estimation using remote video recording of moving human subjects illuminated by ambient light

This paper addresses the video processing problem of passively estimating the heart rate of a human subject that is being video recorded in ambient light in natural conditions in which some motion is allowable. This extends previous work in which control over lighting and motion are required. A mathematical model is developed for the measured pixels on the subject's forehead. This model includes (1) a Markov process that accounts for the large scale but slowly fluctuating variations in reflected light, and (2) a quasi-periodic process to model the relatively small heart beat component. A framework for estimating these components is proposed and results of applying this method to real data recordings is presented. Results are compared to heart rate obtained from a pulse oximeter attached to the subject's finger.