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Automatic Attendance Registration System using Convolutional Neural Networks for Facial Recognition

by Cristian H Sánchez Saquín, Gabriel Rodríguez Feregrino, Alejandro Gómez Hernández
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 43
Year of Publication: 2023
Authors: Cristian H Sánchez Saquín, Gabriel Rodríguez Feregrino, Alejandro Gómez Hernández
10.5120/ijca2023923241

Cristian H Sánchez Saquín, Gabriel Rodríguez Feregrino, Alejandro Gómez Hernández . Automatic Attendance Registration System using Convolutional Neural Networks for Facial Recognition. International Journal of Computer Applications. 185, 43 ( Nov 2023), 28-35. DOI=10.5120/ijca2023923241

@article{ 10.5120/ijca2023923241,
author = { Cristian H Sánchez Saquín, Gabriel Rodríguez Feregrino, Alejandro Gómez Hernández },
title = { Automatic Attendance Registration System using Convolutional Neural Networks for Facial Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 43 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number43/32977-2023923241/ },
doi = { 10.5120/ijca2023923241 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:32.539823+05:30
%A Cristian H Sánchez Saquín
%A Gabriel Rodríguez Feregrino
%A Alejandro Gómez Hernández
%T Automatic Attendance Registration System using Convolutional Neural Networks for Facial Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 43
%P 28-35
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A system has been developed to optimize the attendance taking process in a school using computer vision and artificial intelligence techniques. The traditional method of keeping attendance records using a printed list is time-consuming and prone to errors. To address this issue, a system was implemented using neural networks, specifically YOLOv5 and VGGFace, for face detection and recognition of students in a group at the Technological University of Querétaro. The system operates autonomously throughout the entire class duration, utilizing the Intel RealSense SR300 camera as the video source for capturing images. These images were then labeled using the CVAT software and used to train the neural networks in Google Colab. A Python-based implementation was employed, combining the results of the two neural networks using a voting approach to achieve more accurate identification and recognition of each student. The obtained results, including the recognized students, are sent to the specified email address in the CSV file containing the class schedule, as well as to the email of the responsible teacher. By leveraging artificial intelligence and computer vision, this solution aims to streamline and enhance the attendance taking process in the school. It reduces the time dedicated to this task and minimizes errors associated with the traditional method of using a printed attendance list.

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Index Terms

Computer Science
Information Sciences

Keywords

face recognition convolutional neuronal networks face identification YOLOv5 VGGFace.

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