SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific

Abstract

Remote photoplethysmography (rPPG) based noncontact heart rate (HR) measurement from a face video has drawn increasing attention recently because of its potential applications in many scenarios such as training aid, health monitoring, and nursing care. Although a number of methods have been proposed, most of them are designed under certain assumptions and could fail when such assumptions do not hold. At the same time, while deep learning based methods have been reported to achieve promising results in many computer vision tasks, their use in rPPG-based heart rate estimation has been limited due to the very limited data available in public domain. To overcome this limitation and leverage the strong modeling ability of deep neural networks, in this paper, we propose a novel spatial-temporal representation for the HR signal and design a general-to-specific transfer learning strategy to train a deep heart rate estimator from a large volume of synthetic rhythm signals and a limited number of available face video data. Experiment results on the public-domain databases show the effectiveness of the proposed approach.