Face recognition for Student Attendance using Raspberry Pi

This paper presents development of face recognition for student attendance using Raspberry Pi. Face recognition is a highly efficient and an accurate tool in enhancing security. With nano devices such as Raspberry Pi and Raspberry Pi night vision cameras, lecturers record student attendance to class...

Full description

Bibliographic Details
Published in:APACE 2019 - 2019 IEEE Asia-Pacific Conference on Applied Electromagnetics, Proceedings
Main Author: Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082462343&doi=10.1109%2fAPACE47377.2019.9020758&partnerID=40&md5=b244b9302a7dd1dbcf0357e077218f22
id 2-s2.0-85082462343
spelling 2-s2.0-85082462343
Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
Face recognition for Student Attendance using Raspberry Pi
2019
APACE 2019 - 2019 IEEE Asia-Pacific Conference on Applied Electromagnetics, Proceedings


10.1109/APACE47377.2019.9020758
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082462343&doi=10.1109%2fAPACE47377.2019.9020758&partnerID=40&md5=b244b9302a7dd1dbcf0357e077218f22
This paper presents development of face recognition for student attendance using Raspberry Pi. Face recognition is a highly efficient and an accurate tool in enhancing security. With nano devices such as Raspberry Pi and Raspberry Pi night vision cameras, lecturers record student attendance to class with face-to-face identification systems. Small night-vision raspberry cameras are installed on the classroom door frame in the classroom room to capture video, approved to Raspberry Pi for face detection and recognition. The proposed method is implemented on Raspberry Pi and Raspberry Pi night vision which is tested on various standard datasets. Experimental results validate the efficiency of the proposed recognition method. © 2019 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
spellingShingle Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
Face recognition for Student Attendance using Raspberry Pi
author_facet Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
author_sort Hasban A.S.; Hasif N.A.; Khan Z.I.; Husin M.F.; Rashid N.E.A.; Sharif K.K.M.; Zakaria N.A.
title Face recognition for Student Attendance using Raspberry Pi
title_short Face recognition for Student Attendance using Raspberry Pi
title_full Face recognition for Student Attendance using Raspberry Pi
title_fullStr Face recognition for Student Attendance using Raspberry Pi
title_full_unstemmed Face recognition for Student Attendance using Raspberry Pi
title_sort Face recognition for Student Attendance using Raspberry Pi
publishDate 2019
container_title APACE 2019 - 2019 IEEE Asia-Pacific Conference on Applied Electromagnetics, Proceedings
container_volume
container_issue
doi_str_mv 10.1109/APACE47377.2019.9020758
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082462343&doi=10.1109%2fAPACE47377.2019.9020758&partnerID=40&md5=b244b9302a7dd1dbcf0357e077218f22
description This paper presents development of face recognition for student attendance using Raspberry Pi. Face recognition is a highly efficient and an accurate tool in enhancing security. With nano devices such as Raspberry Pi and Raspberry Pi night vision cameras, lecturers record student attendance to class with face-to-face identification systems. Small night-vision raspberry cameras are installed on the classroom door frame in the classroom room to capture video, approved to Raspberry Pi for face detection and recognition. The proposed method is implemented on Raspberry Pi and Raspberry Pi night vision which is tested on various standard datasets. Experimental results validate the efficiency of the proposed recognition method. © 2019 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1814778507458248704