Heart rate estimation from facial videos using nonlinear mode decomposition and improved consistency check

Abstract

Remote photoplethysmography (rPPG) is a non-contact and noninvasive way of measuring human physiological signals such as the heart rate using the subtle color changes of skin regions. Since the face of a person is generally visible, facial videos can be used for estimating the heart rate remotely. The rigid and non-rigid motions of the face and illumination variations are the main challenges that affect the accuracy of heart rate estimation. In this paper, we present a new method for estimating the heart rate of a person from the skin region of the facial video using nonlinear mode decomposition (NMD), which is a recently proposed blind source separation method and has been shown to be more robust to noise. We also propose a new method (history-based consistency check—HBCC) for selecting the best heart rate candidate after decomposition by minimizing a temporal cost function. Experiments on two datasets show that the proposed method (rPPG-NMD) achieves promising results as compared to several the state-of-the-art methods for rPPG-based heart rate estimation.