An Eye Video Oriented and rPPG-Based Intraocular Pressure Detection Method

Current intraocular pressure (IOP) measurement methods still primarily rely on contact IOP measurement instruments, which are inconvenient for widespread use. This study proposed an innovative method for detecting and classifying IOP using eye videos, based on remote photoplethysmography (rPPG). The IOP-Net model was developed by extracting blood volume pulse (BVP) signals from three regions of interest (ROI)-the pupil, iris and sclera, and training a convolutional neural network (CNN) with four convolutional layers. This model can be used to detect the IOP and determine the classification of normal IOP and high IOP. The root mean square errors (RMSE) on EVIP-1 and EVIP-2 datasets were 3.14 mmHg and 4.19 mmHg, respectively. When the ground truth of IOP is more than 30 mmHg, the accuracy of the model in classifying high IOP reaches 80.25%. The results indicate that this method has promising and potential application for video-based IOP detection and classification.