  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 the 9-direction gaze, we
use a nine-key T9 input method which is widely used in candy bar phones. Bar
phones were very popular in the world decades ago and have cultivated strong
user habits and language models. To train a robust gaze estimator, we created a
large-scale dataset with images of eyes sourced from 25 people. According to
the results from our experiments, our CNN model is able to accurately estimate
different people's gaze under various lighting conditions by different devices.
In considering disable people's needs, we removed the complex calibration
process. The input methods can run in screen mode and portable off-screen mode.
Moreover, The datasets used in our experiments are made available to the
community to allow further experimentation.

Computer Vision and Pattern Recognition