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dc.contributor.authorUçar, Mustafa Uğur
dc.contributor.authorÖzdemir, Ersin
dc.date.accessioned2022-11-15T05:58:07Z
dc.date.available2022-11-15T05:58:07Z
dc.date.issued2022en_US
dc.identifier.citationUçar, M.U., Özdemir, E. (2022). Recognizing Students and Detecting Student Engagement with Real-Time Image Processing. Electronics (Switzerland), 11 (9), art. no. 1500. https://doi.org/10.3390/electronics11091500en_US
dc.identifier.urihttps://doi.org/10.3390/electronics11091500
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2245
dc.description.abstractWith COVID-19, formal education was interrupted in all countries and the importance of distance learning has increased. It is possible to teach any lesson with various communication tools but it is difficult to know how far this lesson reaches to the students. In this study, it is aimed to monitor the students in a classroom or in front of the computer with a camera in real time, recognizing their faces, their head poses, and scoring their distraction to detect student engagement based on their head poses and Eye Aspect Ratios. Distraction was determined by associating the students' attention with looking at the teacher or the camera in the right direction. The success of the face recognition and head pose estimation was tested by using the UPNA Head Pose Database and, as a result of the conducted tests, the most successful result in face recognition was obtained with the Local Binary Patterns method with a 98.95% recognition rate. In the classification of student engagement as Engaged and Not Engaged, support vector machine gave results with 72.4% accuracy. The developed system will be used to recognize and monitor students in the classroom or in front of the computer, and to determine the course flow autonomously.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.isversionof10.3390/electronics11091500en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer visionen_US
dc.subjectEngagement detectionen_US
dc.subjectEye aspect ratioen_US
dc.subjectHead pose estimationen_US
dc.subjectMachine learningen_US
dc.subject.classificationComputer Science
dc.subject.classificationEngineering
dc.subject.classificationPhysics
dc.subject.classificationComputer-Aided Instruction
dc.subject.classificationTutor
dc.subject.classificationComputer-Based Learning
dc.subject.classificationElectrical Engineering, Electronics & Computer Science - Computer Vision & Graphics - Face Recognition
dc.subject.otherPose estimation
dc.subject.otherPerceptipon
dc.subject.otherFace
dc.subject.otherRecognition
dc.subject.otherGaze
dc.subject.otherClassification
dc.subject.otherAttention
dc.subject.otherEyes
dc.titleRecognizing Students and Detecting Student Engagement with Real-Time Image Processingen_US
dc.typearticleen_US
dc.relation.journalElectronics (Switzerland)en_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume11en_US
dc.identifier.issue9en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorÖzdemir, Ersin
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded
dc.relation.indexWeb of Science Core Collection - Social Sciences Citation Index


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