Open Access

ARTICLE

Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks

Debajit Datta¹, Pramod Kumar Maurya¹, Kathiravan Srinivasan², Chuan-Yu Chang^3,*, Rishav Agarwal¹, Ishita Tuteja¹, V. Bhavyashri Vedula¹
1 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
2 School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
3 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
* Corresponding Author: Chuan-Yu Chang. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Big Data in Entrepreneurship)

Computers, Materials & Continua 2021, 68(2), 2545-2561. https://doi.org/10.32604/cmc.2021.015478

Received 23 November 2020; Accepted 08 February 2021; Issue published 13 April 2021

Abstract

The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the VGG16 model. The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores. The proposed system is accurate without the need for complex hardware. Therefore, it can be implemented in educational institutes for the fair conduct of examinations, as well as in other instances where eye gaze detection is required.

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Keywords

Computer vision; convolutional neural network; data integrity; digital examination; eye gaze detection; extraction; information entropy

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