Spatial Feature Extraction for Acute Blood Pressure Fluctuations in Facial Visible Images Using Sparse Coding

Abstract

Remote blood pressure measurement using visible images facilitates routine blood pressure monitoring and leads to early detection of hypertension, a risk factor for lifestyle diseases. The previous study that attempted to estimate blood pressure by applying CNN to facial thermal images found that facial images contain two types of features, physiological responses and expression changes, which need to be separated. In contrast, we found that these features could be separated by using sparse coding on facial thermal images. This study used sparse coding to extract physiological response areas during acute blood pressure fluctuations from facial visible images by examining preprocessing. The results indicated that sparse coding and the proposed preprocessing methods for images were effective. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.