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dc.contributor.authorJain, Shubham
dc.contributor.authorFallon, Enda
dc.identifier.citationJain, S., Fallon, E. (2019). Low-cost gaze detection with real-time ocular movement using coordinate-convolutional neural networks. International Journal of Simulation Systems, Science & Technology. 20(5). 5.1-5.6. doi: 10.5013/IJSSST.a.20.05.05en_US
dc.description.abstractDetection of ocular-movements unfolds various possibilities in computer vision but requires large datasets, expensive hardware and computational power. Prior research substantiates the belief that Convolutional Neural Network provides the highest recognition rate compared to traditional techniques, but they begin to overfit after achieving a certain accuracy due to the coordinate-transform-problem. Different image conditions like variation-in-viewpoint or illumination can be pragmatic for image processing and require on-device calibration. This paper proposes a framework that works with low-computational-complexity in varied environmental conditions to provide efficient gaze estimations that points out screen coordinates in real-time. We use a depth-wise convolution, an expansion and a projection layer along-with coordinate-channels to improve classification. The model is experimented against different environmental conditions, multiple subjects, image augmentation and different data sizes in real-time to estimate the coordinate classes using eye-movements on a standard web camera, yielding better accuracy and preventing overfitting of model with fewer hardware requirements.en_US
dc.publisherUnited Kingdom Simulation Societyen_US
dc.relation.ispartofInternational Journal of Simulation Systems, Science & Technologyen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectGaze detectionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.titleLow-cost gaze detection with real-time ocular movements using coordinate-convolutional neural networksen_US
dc.conference.locationKuala Lumpuren_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.identifier.conferenceCICSyN 2019: 9th International Conference on Computational Intelligence, Communication Systems and Networks
dc.identifier.doidoi: 10.5013/IJSSST.a.20.05.05en_US
dc.subject.departmentSoftware Research Institute AITen_US

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International