Time-varying rPPG signal separation via block-sparse signal model

TL;DR

This paper introduces a novel method for extracting rPPG signals from facial videos by modeling their quasi-periodic nature as a block-sparse structure in the time-frequency domain, improving robustness to illumination noise.

Contribution

It proposes a time-varying signal separation framework that exploits block-sparse modeling of quasi-periodic rPPG signals for enhanced non-contact cardiac monitoring.

Findings

Effective in extracting rPPG signals under illumination fluctuations

Demonstrated improved accuracy on a public dataset

Utilizes a novel block-sparse signal model in the time-frequency domain

Abstract

Remote photoplethysmography (rPPG) enables non-contact measurement of cardiac pulse signals by analyzing subtle color changes in facial videos. Nevertheless, extracting rPPG signals remains challenging because of their extremely weak signal strength and susceptibility to illumination noise. In this paper, we propose an rPPG signal extraction method that exploits the quasi-periodic characteristics of rPPG signals. Our approach models quasi-periodicity of the rPPG signal, which arises from the stable cardiac cycle, as a block-sparse structure in the time-frequency domain. To incorporate a block-sparse model and enable adaptive signal separation under illumination fluctuations, we construct a time-varying signal separation framework. Experiments using a public dataset demonstrate the effectiveness of our method.