Efficient Eye Typing with 9-direction Gaze Estimation
Chi Zhang, Rui Yao, Jinpeng Cai

TL;DR
This paper introduces a low-cost, robust eye typing system using a 9-direction gaze estimation with CNNs, eliminating calibration and enabling effective text input for disabled users.
Contribution
A novel 9-direction gaze estimation method with CNNs that improves accuracy and robustness, removing the need for calibration in eye typing systems.
Findings
High accuracy in gaze estimation across lighting conditions
Effective text input using a 9-key T9 method
Robust performance without calibration
Abstract
Vision based text entry systems aim to help disabled people achieve text communication using eye movement. Most previous methods have employed an existing eye tracker to predict gaze direction and design an input method based upon that. However, these methods can result in eye tracking quality becoming easily affected by various factors and lengthy amounts of time for calibration. Our paper presents a novel efficient gaze based text input method, which has the advantage of low cost and robustness. Users can type in words by looking at an on-screen keyboard and blinking. Rather than estimate gaze angles directly to track eyes, we introduce a method that divides the human gaze into nine directions. This method can effectively improve the accuracy of making a selection by gaze and blinks. We build a Convolutional Neural Network (CNN) model for 9-direction gaze estimation. On the basis of…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Advanced Computing and Algorithms · Glaucoma and retinal disorders
