This paper is published in Volume-7, Issue-1, 2021
Area
Computer Engineering
Author
Vaibhav Ghadiali, Jevin Jain, Meet Nandu
Org/Univ
Shah and Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India
Pub. Date
12 February, 2021
Paper ID
V7I1-1231
Publisher
Keywords
Attendance System, CNN (Convolutional Neural Network), Deep Learning

Citationsacebook

IEEE
Vaibhav Ghadiali, Jevin Jain, Meet Nandu. Smart centralized attendance management system, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vaibhav Ghadiali, Jevin Jain, Meet Nandu (2021). Smart centralized attendance management system. International Journal of Advance Research, Ideas and Innovations in Technology, 7(1) www.IJARIIT.com.

MLA
Vaibhav Ghadiali, Jevin Jain, Meet Nandu. "Smart centralized attendance management system." International Journal of Advance Research, Ideas and Innovations in Technology 7.1 (2021). www.IJARIIT.com.

Abstract

The management of the attendance can often be a good burden on the lecturers if it is done manually. To resolve this problem, a smart and auto attendance management system is being utilized. But authentication is a crucial issue in this system. Biometrics are generally used to execute a smart attendance system. Face recognition is one of the biometric to be used. The human face is a vital authentication parameter, it has many applications in other fields such as video monitoring and CCTV footage system, access systems present indoors and network security, identification of people, electronics, and validation of identities. By using a similar framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved. The important implementation steps used in this type of system are face detection and recognizing the detected face. This paper proposes a model for implementing an automated attendance management system for students of a class by making use of face recognition technique, by using Convolutional Neural Network (CNN). After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student's faces. This model will be a successful technique to manage the attendance and records of students.
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