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 situation...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.展开更多
Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tra...Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases,and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective.We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum,known as the EDVAM.In addition,a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements.This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.展开更多
基金funded by the“Intelligent Recognition Industry Service Research Center”from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan.Grant Number:N/A and the APC was funded by the aforementioned Project.
文摘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.
基金Project supported by the National Natural Science Foundation of China(No.61802341)the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(No.2018AAA0100703)the Research Innovation Plan of the Ministry of Education of China,and the Provincial Key Research and Development Plan of Zhejiang Province,China(No.2019C03137)。
文摘Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases,and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective.We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum,known as the EDVAM.In addition,a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements.This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.
