Paper Title
Class Attendance Monitoring System Using Face Recognition
Article Identifiers
: Keywords
Face Recognition, Attendance marking, OpenCV, Flask, K-Nearest Neighbors.
Abstract
Marking attendance in a classroom during a session is not only a tedious task but also a time consuming one. The presence of abnormally large number of students during the session leads to high chances of proxy attendance. Marking presence with conventional methods has been a complicated task. The increased demand in efficient and automatic techniques of marking attendance is a problematic aspect in the area of face recognition. Earlier, the issue of automatic attendance marking has been managed through standard biometrics like fingerprint, RFID, Iris Biometric, etc. Anyway, these techniques lack reliability. This paper endeavours to design an automated system that records the attendance with the usage of facial recognition. The system allows the user to post name, Id and images while adding a new student data and stores the images based on the coordinates of the face location in the database. A facial recognition model is trained based on the dataset of images. It initializes a K-Nearest Neighbors classifier with a specified number of neighbors and fits the model on the known faces and names. After training the model, attendance can be marked along with the time of registered students in an excel sheet which can be viewed and downloaded.
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How To Cite (APA)
R. Thanmayi, Mrs. D. Anusha, A. Charan Teja, G. Nitish Mouli, & V. Varun Raju (March-2023). Class Attendance Monitoring System Using Face Recognition. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), e330-e335. https://ijnrd.org/papers/IJNRD2303444.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : e330-e335
Other Publication Details
Paper Reg. ID: IJNRD_189885
Published Paper Id: IJNRD2303444
Downloads: 000122253
Research Area: Engineering
Author Type: Indian Author
Country: VISHAKHAPATNAM, ANDHRA PRADESH, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303444.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303444
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