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. INDEX TERMS Gaze-writing, input technique, MNIST, Eye tracking, machine learning.

Eye-Tracking Feature Extraction for Biometric Machine Learning

“…Based on Table 1, most of the studies showed a positive result from the performance. The highest accuracy obtained was 99.3 (Lagodzinski et al, 2018) and 99.21% (Yoo et al, 2019), with the features of EOG and gaze position, respectively. The study of Guo et al (2021) has the lowest accuracy among the studies found, which achieved an accuracy of 49.32%.…”

“…In a study by Kacur et al (2019), the authors presented a method for the detection of schizophrenia disorders using gaze position with the Rorschach Inkblot Test. In a study by Yoo et al (2019), a gazewriting classification technique for numeric gaze-written entry is proposed.…”

Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality

“…Another group of research has focused on developing prediction models. Studies have employed machine learning algorithms (e.g., Support Vector Machine (SVM), Logistic Regression, Random Forest, k -Nearest Neighbors, Na ve Bayes), and more recent studies have begun to utilize deep learning methods (Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)) to extract deep features of the data and construct a more complicated learning models [ 18 , 21 , 22 ]. These studies have utilized various types of features extracted from sensor signals as the data for their prediction models and demonstrated high possibilities of predicting users’ states or behaviors through their models.…”