Robust algorithm for remote photoplethysmography in realistic conditions

Over the last decade remote photoplethysmography (rPPG) algorithms have been developed extensively. As a result, pulse rate can now be accurately estimated by video data for still subjects. However, in realistic conditions both the accuracy of these algorithms and benchmark datasets are far from perfect. In this paper we propose a new rPPG method which enables heart rate detection by video from a standard webcam. The algorithm is robust with respect to such factors as illumination changes or the subject's movements, and can track fast pulse rate changes. To do that, the algorithm determines the approximate value of the pulse rate and then specifies it with high time resolution. In order to comprehensively study the proposed method, we collected a new dataset consisting of videos recorded in various challenging conditions of several categories as well as reference photoplethysmograms recorded synchronously with a contact pulse oximeter. The proposed method showed high performance under all conditions including blinking illumination, speech and large-amplitude movements. We tested two simplified versions of the algorithm, which provided competitive scores as well. However, with human movement videos the full method showed better results than its simplified versions (p<0.001). The proposed algorithm was tested on the existing UBFC-RPPG database and compared with previous methods. Our method showed high results (2.10 MAE, 3.43 RMSE). (C) 2020 Elsevier Inc. All rights reserved.