A CNN based real-time eye tracker for web mining applications

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridari, ali/0000-0002-5071-6790
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorDonuk, Kenan
dc.contributor.authorAri, Ali
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:51:54Z
dc.date.available2024-08-04T20:51:54Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractEye gaze tracking is an increasingly important technology in the field of human-computer interaction. Individuals' preferences, tendencies, and attention can be measured by processing the data obtained from face and eye images. This technology is used in advertising, market research, web page design, education, learning methods, and various neurological-psychiatric studies of medical research. Many different methods have been used in eye gaze tracking tasks. Today, commonly model-shape and appearance-based methods are used. Model-shape based methods require less workload than appearance-based methods. But it is more sensitive to environmental conditions. Appearance-based methods require powerful hardware, but they are less susceptible to environmental conditions. Developments in technology have paved the way for applying appearance-based models in eye gaze tracking. In this paper, a CNN-based real-time eye tracking system was designed to overcome environmental problems in eye gaze tracking. The designed system is used to determine the areas of interest of the user in web pages. The performance of the designed CNN-based system is evaluated during the training and testing phases. In the training phase, the difference between the desired and determined points on the screen is 32 pixels and in testing phase, the difference between the desired and determined points on the screen is 53 pixels. The results of the test trials have shown that the proposed system could be used successfully in eye tracking studies on web pages.en_US
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit (BAP) [FDK-2020-2110]en_US
dc.description.sponsorshipThis study was supported by Inonu University Scientific Research Projects Coordination Unit (BAP) with the project coded FDK-2020-2110.en_US
dc.identifier.doi10.1007/s11042-022-13085-7
dc.identifier.endpage39120en_US
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue27en_US
dc.identifier.scopus2-s2.0-85128909145en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage39103en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-022-13085-7
dc.identifier.urihttps://hdl.handle.net/11616/100617
dc.identifier.volume81en_US
dc.identifier.wosWOS:000794850900003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectCNNsen_US
dc.subjectEye gaze trackingen_US
dc.subjectWeb data miningen_US
dc.titleA CNN based real-time eye tracker for web mining applicationsen_US
dc.typeArticleen_US

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