IoT Based Smart Manifold Attendance System

dc.contributor.authorSen, Raunak
dc.contributor.authorPrabhakaran, Thejus
dc.contributor.authorKumar, Raman
dc.date.accessioned2020-10-07T10:00:11Z
dc.date.available2020-10-07T10:00:11Z
dc.date.issued2020-10-07T10:00:11Z
dc.description.abstractThe traditional attendance technique involves human to human interaction (attendance slip being passed from teacher to peon) and human to computer interaction (teacher updates the attendance in the system). While a normal working day comprises of 7 to 8 hours of class, even if a minimal of 10 minutes each is devoted for attendance, it sums up to approximately 80 minutes per day. This is truly increasing the plight of the teacher. Advancements in attendance included techniques like retinal scanning, fingerprint scanning, face recognition. Smart Manifold Attendance using Real Time Face Recognition is a real world solution to the existing issues in traditional methods. This system consists of four phases - face database, face detection, face recognition and marking attendance. Image acquisition is accomplished using a camera fitted in the classroom. The system first stores the faces in the database. The faces of students are detected / located from the image captured in real time. The detected faces are then compared with those stored in the database during face recognition. If the system recognizes faces, the attendance gets marked immediately in the excel sheets. Also, we send hourly text messages to parents, of wards that are absent. Teachers’ attendance is monitored as well. As per VTU, 52 hours need to be completed per subject and this purpose is also served in the proposed system on a OPEN CV platform.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/13465
dc.language.isoen_USen_US
dc.subject1NH16EE741en_US
dc.subject1NH16EE752en_US
dc.subject1NH16EE739en_US
dc.subject1NH16EE724en_US
dc.subjectIoT Based Smart Manifold Attendance Systemen_US
dc.titleIoT Based Smart Manifold Attendance Systemen_US
dc.typeLearning Objecten_US
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