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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Research Article

Attendance Monitoring System Design Based on Face Segmentation and Recognition

Author(s): Valaparla Rohini*, Mummaneni Sobhana and Ch Smitha Chowdary

Volume 17, Issue 2, 2023

Published on: 21 June, 2022

Article ID: e010422203006 Pages: 11

DOI: 10.2174/1872212116666220401154639

Price: $65

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Abstract

Aim: The proposed work aim was to monitor real-time attendance using face recognition in every institutional sector. It is one of the key concerns in every organization.

Background: Nowadays, most organizations spend a lot of time marking attendance for a large number of individuals manually. Many technologies like Radio Frequency Identification (RFID) and biometric systems are introduced to overcome the manual attendance system. when using these RFID and biometric people need to stand in queue to make their presence.

Objective: The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems.

Methods: The proposed work was done through face recognition using Machine Learning. Face recognition is a part of biometric characteristic of a human. It was easy to process than other biometric measurements like fingerprint, iris scan, hand scan, retina scan. The Haarcascade classifier will detect a face, and the LBPH algorithm will recognize the face. The experiment performs on the creation of real-time face data.

Results: Using the web camera connected to the computer, face detection and recognition are performed, and recognized faces mark as attended. Here, the admin module and teacher modules are implemented with different functionalities to monitor attendance.

Conclusion: Experiment results get 94.5% accuracy in face detection and 98.5% accuracy in face recognition by using the Haarcascade classifier and LBPH algorithm. This application system will be simple to implement, accurate, and efficient in monitoring attendance in real-time.

Keywords: Face detection, recognition, attendance monitoring, haarcascade classifier, LBPH algorithm, real-time.

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