Real-time Iris Center Detection Based on Convolutional Neural Networks

Abstract

It is an active field of study in studies where the iris center is referenced, such as iris center detection, gaze tracking, driver fatigue detection. In this study, an approach for real-time detection of iris centers based on convolutional neural networks is presented. The GI4E dataset was used as the dataset for the proposed approach. Experimental results estimated the test data of the proposed convolutional neural network model with an accuracy of 97.2% based on the 0.025 error corresponding to the closest position to the iris center according to the maximum normalized error criteria. The study was also tested in real time with a webcam built into the computer. While the test accuracy is satisfactory, real-time speed performance needs to be improved.