In recent years, the physiological measurement based on remote photoplethysmography has attracted wide attention, especially since the epidemic of COVID-19. Many researchers paid great efforts to improve the robustness of illumination and motion variation. Most of the existing methods divided the ROIs into many sub-regions and extracted the heart rate separately, while ignoring the fact that the heart rates from different sub-regions are consistent. To address this problem, in this work, we propose a structural sparse representation method to reconstruct the pulse signals (SSR2RPS) from different sub-regions and estimate the heart rate. The structural sparse representation (SSR) method considers that the chrominance signals from different sub-regions should have a similar sparse representation on the combined dictionary. Specifically, we firstly eliminate the signal deviation trend using the adaptive iteratively re-weighted penalized least squares (Airpls) for each sub-region. Then, we conduct the sparse representation on the combined dictionary, which is constructed considering the pulsatility and periodicity of the heart rate. Finally, we obtain the reconstructed pulse signals from different sub-regions and estimate the heart rate with a power spectrum analysis. The experimental results on the public UBFC and COHFACE datasets demonstrate the significant improvement for the accuracy of the heart rate estimation under realistic conditions.
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