Block-based adaptive ROI for remote photoplethysmography

Remote photoplethysmography (rPPG) can achieve contactless human vital signs monitoring, but its signal quality is limited by the remote operation nature. In practical applications, improving the rPPG signal quality becomes an essential task. As a remote imaging technique, rPPG utilizes a camera to capture a video of a skin area, especially the facial area, then focuses on a particular sub-area as the region of interest (ROI). In this paper, we investigated a novel adaptive ROI (AROI) approach for improving the rPPG signal quality. In this approach, block-based spatial-temporal division is performed on a captured face video. Based on these segmented video pipelines, the spatial-temporal quality distribution of the rPPG signals is estimated using a signal-to-noise ratio (SNR) feature. Afterwards, AROIs are calculated through mean-shift clustering and adaptive thresholding in SNR maps. As the AROI can be dynamically adjusted according to the spatial-temporal quality distribution of rPPG signals on the face, the quality of the final recovered rPPG signal is improved. The performance of the proposed AROI approach was evaluated with both still and moving subjects. Compared to conventional ROI methods for rPPG, the proposed AROI obtained a higher accuracy in heart rate measurement. And the state-of-the-art motion-resistant rPPG techniques can be effectively enhanced through being integrated with the AROI.