The novel EEMD-MCCA method improves the robustness of remote photoplethysmography against spatially uneven illumination for heart rate monitoring.
Remote photoplethysmography (rPPG) is an attractive video-based technique to monitor heart rate (HR) information in telehealth screening. The subjects are required to stay stationary in the remote screening scenario and ambient lights become the main interference source of measuring HR with rPPG. In this paper, we introduce a novel approach robust against spatially uneven illumination interference in rPPG by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We adopt the following procedures to ensure that the pulses dominate the correlations across multiple signal sets while the illumination noises are as diverse as possible. Specifically, a group of optimal regions of interest (ROIs) are selected according to the quality indicators defined from the green channel in each candidate ROI. A multi-channel signal set is then constructed by decomposing the green signal with EEMD. Only those intrinsic mode functions (IMFs) with the dominant frequencies falling into the interested HR range are utilized as the input of MCCA. The canonical variables (CVs) with the highest cross correlations are derived as the underlying candidate pulses. Finally, fast Fourier transform (FFT) is employed to calculate the dominant frequency and the target HR is determined based on both the quasi-periodic property and the continuity of HR. The proposed EEMD-MCCA method is validated on both the in-house BSIPL-RPPG and the public COHFACE databases, which achieves superior performance over several typical rPPG methods. This study will provide a promising tool for realistic rPPG applications in telehealth screening.
Song et al. (Mon,) studied this question.