Paper Info
Title
The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface.
Abstract
Recently, many applications are developed in numerous domains with various environments. Since some environments require hands-free applications, new technology is needed for the input interfaces other than the mouse and keyboard. Therefore, to meet the needs, many researchers have begun to investigate the gaze and voice for the input technology. In particular, there are many approaches to render virtual keyboards with the gaze. However, since the virtual keyboards hide the screen space, this technique can only be applied in limited environments. In this paper, we propose a classification technique for gaze-written numbers as the hands-free interface. Since the gaze-writing is less accurate compared to the virtual keyboard typing, we apply the convolutional neural network (CNN) deep learning algorithm to recognize the gaze-writing and improve the classification accuracy. Besides, we create new gaze-writing datasets for training, gaze MNIST (gMNIST), by modifying the MNIST data with features of the gaze movement patterns. For the evaluation, we compare our approach with the basic CNN structures using the original MNIST dataset. Our study will allow us to have more options for the input interfaces and expand our choices in hands-free environments.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2909573
IEEE ACCESS
Keywords
Field
DocType
Gaze-writing,input technique,MNIST,Eye tracking,machine learning
MNIST database,Gaze,Convolutional neural network,Computer science,Human–computer interaction,Virtual keyboard,Artificial intelligence,Deep learning,Screen space,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Sangbong Yoo102.37
Dae Kyo Jeong221.06
Yun Jang330225.63