Research study on eye gaze estimation using neural network

Since the 1800s, studies of eye movement and eye tracking has long attracted interest from various researchers and scientists. However, eye tracking technology has slowly moved towards eye gaze estimation technology instead as it has been proven to be more useful in real-world applications. Th...

Full description

Saved in:
Bibliographic Details
Main Author: Hu, Qingyao
Other Authors: Lee Bu Sung, Francis
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77032
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-77032
record_format dspace
spelling sg-ntu-dr.10356-770322023-03-03T20:42:59Z Research study on eye gaze estimation using neural network Hu, Qingyao Lee Bu Sung, Francis School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Since the 1800s, studies of eye movement and eye tracking has long attracted interest from various researchers and scientists. However, eye tracking technology has slowly moved towards eye gaze estimation technology instead as it has been proven to be more useful in real-world applications. This project aims to collect eye gaze data, explore methods for eye gaze estimation using convolutional neural network and implement the methods on mobile devices to explore the application of the eye gaze estimation technology. Neural networks are a part of deep learning technology which has been adopted by various major IT companies around the globe. It utilizes artificial neurons to learn the patterns that exist in complex data such as image and audio to predict certain results. To collect eye gaze data, two Android applications, GazeCollect and Gazestimate was developed to collect 2 different types of eye gaze data. GazeCollect gathers data of participants looking at random red dots displayed on the mobile device. On the other hand, Gazestimate gathers data through a simple card matching game which participants are required to complete. For this project, classification method was explored in this project to predict eye gaze location on mobile devices by segmenting the mobile device’s screen into 32 areas. A combination of image and scalar inputs are used as input for the convolutional neural network model to compensate for the difference in device orientation and size. Resnet is used in the convolutional network model architecture as Resnet has been proven to perform accurately in image recognition task. The classification method explored yielded a best accuracy of 47.76 to 49.37% accuracy on predicting eye gaze location. Finally, Gazestimate simple card matching game was modified to be interactive using eye gaze estimation. Additional functionality was added to Gazestimate to display the exact eye gaze prediction on the device’s screen by displaying red dots too. These functionalities were used to showcase eye gaze estimation techniques during Nanyang Technological University’s Open House 2019 Bachelor of Engineering (Computer Engineering) 2019-05-02T07:17:08Z 2019-05-02T07:17:08Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77032 en Nanyang Technological University 55 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Hu, Qingyao
Research study on eye gaze estimation using neural network
description Since the 1800s, studies of eye movement and eye tracking has long attracted interest from various researchers and scientists. However, eye tracking technology has slowly moved towards eye gaze estimation technology instead as it has been proven to be more useful in real-world applications. This project aims to collect eye gaze data, explore methods for eye gaze estimation using convolutional neural network and implement the methods on mobile devices to explore the application of the eye gaze estimation technology. Neural networks are a part of deep learning technology which has been adopted by various major IT companies around the globe. It utilizes artificial neurons to learn the patterns that exist in complex data such as image and audio to predict certain results. To collect eye gaze data, two Android applications, GazeCollect and Gazestimate was developed to collect 2 different types of eye gaze data. GazeCollect gathers data of participants looking at random red dots displayed on the mobile device. On the other hand, Gazestimate gathers data through a simple card matching game which participants are required to complete. For this project, classification method was explored in this project to predict eye gaze location on mobile devices by segmenting the mobile device’s screen into 32 areas. A combination of image and scalar inputs are used as input for the convolutional neural network model to compensate for the difference in device orientation and size. Resnet is used in the convolutional network model architecture as Resnet has been proven to perform accurately in image recognition task. The classification method explored yielded a best accuracy of 47.76 to 49.37% accuracy on predicting eye gaze location. Finally, Gazestimate simple card matching game was modified to be interactive using eye gaze estimation. Additional functionality was added to Gazestimate to display the exact eye gaze prediction on the device’s screen by displaying red dots too. These functionalities were used to showcase eye gaze estimation techniques during Nanyang Technological University’s Open House 2019
author2 Lee Bu Sung, Francis
author_facet Lee Bu Sung, Francis
Hu, Qingyao
format Final Year Project
author Hu, Qingyao
author_sort Hu, Qingyao
title Research study on eye gaze estimation using neural network
title_short Research study on eye gaze estimation using neural network
title_full Research study on eye gaze estimation using neural network
title_fullStr Research study on eye gaze estimation using neural network
title_full_unstemmed Research study on eye gaze estimation using neural network
title_sort research study on eye gaze estimation using neural network
publishDate 2019
url http://hdl.handle.net/10356/77032
_version_ 1759854734371454976