Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System

Abstract

In augmented reality (AR) or virtual reality (VR) systems, eye tracking is a key technology and requires significant accuracy as well as real-time operation. Many techniques for detecting pupil centers with error range of iris radius have been developed, but few techniques have precise performance with error range of pupil radius. In addition, the conventional methods rarely guarantee real-time pupil center detection in a general-purpose computer environment due to high complexity. Thus, we propose more accurate pupil center detection by improving the representation quality of the network in charge of pupil center detection. This is realized by representation learning based on mutual information. Also, the latency of the entire system is greatly reduced by using non-local block and self-attention block with large receptive field, which makes it accomplish real-time operation. The proposed system not only shows real-time performance of 52 FPS in a general-purpose computer environment but also provides state-of-the-art accuracy in terms of fine level index of 96.71%, 99.84% and 96.38% for BioID, GI4E and Talking Face Video datasets, respectively.