Continuous heart rate measurement from face: A robust rPPG approach with distribution learning

Non-contact heart rate (HR) measurement via remote photoplethysmography (rPPG) has drawn increasing attention. While a number of methods have been reported, most of them did not take into account the continuous HR measurement problem, which is more challenging due to limited observed video frames and the requirement of speed. In this paper, we present a real-time rPPG method for continuous HR measurement from face videos. We use a multi-patch ROI strategy to remove outlier signals. Chrominance feature is then generated from each ROI to reduce the color channel magnitude differences, which is followed by temporal filtering to suppress the artifacts. In addition, considering the temporal relationship of neighboring HR rhythms, we learn a HR distribution based on historical HR measurements, and apply it to the succeeding HR estimations. Experiment results on the public-domain MAHNOB-HCI database and user tests with commodity webcams show the effectiveness of the proposed approach.