Non-Contact Heart Rate Measurement From Facial Video Data Using a 2D-VMD Scheme

Non-contact human body vital parameter measurements are getting more attention and have been extensively studied in a short span of time. One of such techniques is the heart rate (HR) estimation based on video recording of human face known as remote photoplethysmogram (rPPG). Usually, the recorded video gets contaminated due to illumination variation of ambient light, motion artifacts, and other environmental factors. Thus, extracting a reliable rPPG signal is a challenging task. In this paper, a novel spatial-temporal filtering method is proposed that utilizes 2D variational mode decomposition (2D-VMD) along with azimuthally averaged power spectrum density (AAPSD) and multimode kurtosis to extract a reliable rPPG signal. The robustness of the proposed algorithm is tested and validated using our own database and the publicly available standard dataset. The obtained experimental results are compared with the reference PPG signal measurements. Also, the proposed method is compared with the well-established independent component analysis (ICA)-based method. The performance results show that the non-contact HR estimated by the proposed method dramatically reduces the error and it proves our method to be superior than the existing method.