Paper Title
Student Attendance System Using Face Recognition
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Authors
Keywords
Computer vision, OpenCV, NumPy, Local Binary Pattern Histogram, Harr Cascade.
Abstract
In schools or colleges, we have two types of attendance systems, one is the teacher or professor calling students' names and the second is in front of students' names the student sign. We came up with a new attendance system model. It will detect the student's face and will send a WhatsApp message to the registered contact number of their parents and email to the teacher or professor. Humans can recognize faces and complexions. The computer is not intelligent. We must make it smart by creating and training the model. Video and Image are the core of computer vision. We must connect Python to a webcam, for creating images we need some drivers and a library. With the help of the OpenCV-python library, we can connect to a webcam. Every webcam has unique numbers. The internal webcam number is zero. The external webcam number is one. In this, we will use the Harras Cade model for face detection. Computers understand binary numbers and every binary number has unique pixels. It is consequently suggested that the article address each of these issues. Attendance reports will be generated and saved in Excel format following face recognition. The system is tested in a variety of settings including changes in lighting, head motions and the distance between the learner and the cameras.
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How To Cite (APA)
Deepali Mishra & Manu Mishra (January-2024). Student Attendance System Using Face Recognition. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(1), d232-d236. https://ijnrd.org/papers/IJNRD2401332.pdf
Issue
Volume 9 Issue 1, January-2024
Pages : d232-d236
Other Publication Details
Paper Reg. ID: IJNRD_213245
Published Paper Id: IJNRD2401332
Downloads: 000122256
Research Area: Electronics & Communication Engg.Â
Author Type: Indian Author
Country: Satna, Madhya Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2401332.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2401332
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