  A head-mounted display (HMD) is a portable and interactive display device.
With the development of 5G technology, it may become a general-purpose
computing platform in the future. Human-computer interaction (HCI) technology
for HMDs has also been of significant interest in recent years. In addition to
tracking gestures and speech, tracking human eyes as a means of interaction is
highly effective. In this paper, we propose two UnityEyes-based convolutional
neural network models, UEGazeNet and UEGazeNet*, which can be used for input
images with low resolution and high resolution, respectively. These models can
perform rapid interactions by classifying gaze trajectories (GTs), and a
GTgestures dataset containing data for 10,200 "eye-painting gestures" collected
from 15 individuals is established with our gaze-tracking method. We evaluated
the performance both indoors and outdoors and the UEGazeNet can obtaine results
52\% and 67\% better than those of state-of-the-art networks. The
generalizability of our GTgestures dataset using a variety of gaze-tracking
models is evaluated, and an average recognition rate of 96.71\% is obtained by
our method.
