Smart Attendance System Using Deep Learning Technique

Appearance is a compulsory necessity of every organization and preserving attendance register daily is a tough and time-consuming duty. All organization has sanctioned their own technique of enchanting attendance i.e., calling the names or by passing the sheets. Several popular automatic attendance...

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Bibliographic Details
Published in:Turkish journal of computer and mathematics education 2021-01, Vol.12 (10), p.1367-1373
Main Authors: Aishwarya, E, Kumaravel, K, Suthesh, R Mohamed, Poornima, S, Poonguzhali, R
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Face recognition
Feature extraction
Machine learning
Mathematics
Radio frequency identification
Security
Teaching Methods
Time and attendance systems
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Summary:Appearance is a compulsory necessity of every organization and preserving attendance register daily is a tough and time-consuming duty. All organization has sanctioned their own technique of enchanting attendance i.e., calling the names or by passing the sheets. Several popular automatic attendance systems currently in use are RFID, IRIS, BIOMETRIC etc. Conversely, constructioncrocodile is needed in these cases thus requires more time and it is indiscreet in flora. If proximate is any damage to RFID card, it may result in an improper attendance. Positioning these organisms on large scale are not cost efficient and also takes lot of time to post attendance. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors like fake attendance, high cost, and time consumption are avoided.In this paper, the smart machine learning based face recognition approach has been proposed. The database has beencreated by capturing the faces of the ratified students. The face is perceived using deep learning-based slant. The collectedimageries then stored as a database with respective labels. The features are extracted using Haar-like features and Deep Learning Algorithm. The proposed approach achieves the recognition rate of 98% for CNN and 92% for the Haar-like features and Deep Learning Algorithm.
ISSN:1309-4653