=Paper=
{{Paper
|id=Vol-3885/paper25
|storemode=property
|title=Development of a Lecture Attendance Registration System Based on Facial Recognition
|pdfUrl=https://ceur-ws.org/Vol-3885/paper25.pdf
|volume=Vol-3885
|authors=Irakli Basheleishvili,Giorgi Vardosanidze,Giorgi Makharoblidze
|dblpUrl=https://dblp.org/rec/conf/ivus/BasheleishviliV24
}}
==Development of a Lecture Attendance Registration System Based on Facial Recognition==
https://ceur-ws.org/Vol-3885/paper25.pdf
Development of a Lecture Attendance Registration System
Based on Facial Recognition*
Irakli Basheleishvili1,∗,†, Giorgi Vardosanidze2,† and Giorgi Makharoblidze2,†
1
Akaki Tsereteli State University, Kutaisi, 4600, Georgia
2
Students, Akaki Tsereteli State University, Kutaisi, 4600, Georgia
Abstract
The paper deals with the development of a registration system based on facial recognition to
be used in the educational process, using which it will be possible to register and record the
attendance of both students and professors at lectures. Based on the data recorded by the
system, it will be possible to conduct lectures and monitor student attendance, which is one of
the important tasks of an educational institution.
Keywords
Facial recognition, Attendance system, lecture, students.
1. Introduction
Along with the rapid development of modern technologies, the demand for them in all spheres of
human activity is increasing. This is because the level of introduction of modern technologies in this
or that field determines their effective and successful activity.
Today, education is one of the most important and responsible areas. In which the introduction and
use of modern techniques and technologies are vital.
Educational institutions are trying to develop management systems based on modern
technologies, with the help of which they will be able to effectively solve various tasks in the process
of activity.
One of the important tasks of educational institutions is to register and monitor attendance at
lectures. The task of recording attendance is generally an actual task for both educational and other
institutions.
To solve the mentioned problem, educational institutions use different approaches, but they are
also characterized by other shortcomings, which reduce the validity of accounting.
Due to the urgency of the attendance registration task, the goal of our topic is to develop a face
recognition-based attendance registration system. which will give us the means to record both the
professor-teachers' conducting/not conducting the lecture and the duration of the conducted time, as
well as the student's attendance/non-attendance at the lecture and the duration of the attendance
time. This will allow us to maximize the validity of the registration and reduce the costs associated
with registration.
The novelty of the research lies in the proposal of a new approach to attendance registration and
the development of a software system for its implementation.
2. Current approaches to recording attendance at lectures
Different approaches are used to record attendance at lectures, including:
*
IVUS2024: Information Society and University Studies 2024, May 17, Kaunas, Lithuania
1,∗
Corresponding author
†
These author contributed equally.
Irakli.basheleishvili@atsu.edu.ge(I.Basheleishvili);vardosanidze.giorgi3@atsu.edu.ge(G.Vardosanidze);
makharoblidze.giorgi@atsu.edu.ge (G. Makharoblishvili)
0000-0002-4429-7577 (I. Basheleishvili)
©️ 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Accounting for the delivery of a lecture by a human, which is routine work, and due to
subjective factors, the validity of recorded data is very low. Using the traditional registration
journal by the lecturer to record the student's attendance at the lecture. This approach to
recording students' daily attendance can lead to errors and waste a lot of time.
Cards system [1,4] - This refers to the development of a system that allows the use of electronic
cards to record attendance at lectures, both for lecturers and students. For this, all of them must
have a specially made electronic card. The disadvantages of this type of approach are:
increased costs caused by the production of cards, since their production requires a certain
amount of money, in case the card is lost, the lecturer and the student will not be able to attend
the lecture, the low reliability of registration, since one student can register several students to
attend the lecture using their cards, so that the said students were not in the educational
institution.
Use of biometric system [2,3,8,9,10] - biometric identifier has an undoubted advantage
compared to other approaches, as far as: it is impossible to steal, it is impossible to forget, it is
impossible to transfer to another person, it is convenient and comfortable, it is more reliable. In
the educational process, biometric technology can be used in different ways. But the biometric
system also has some drawbacks: in the case of a large number of students at the lecture, the
time to identify them will increase significantly, which may lead to queues at the biometric
identification device, a student or lecturer may register with the biometric identification device
but not attend the lecture.
Considering the shortcomings of the approaches listed above, it can be said that it is still not
possible to record attendance at the lecture as valid as possible.
Therefore, we aim to propose a new approach, which involves the development of a lecture
attendance registration system based on facial recognition.
3. Methodology
Our proposed methodology is based on the face recognition task, through which it will be possible
to identify the student. Facial recognition is based on a convolutional neural network.
A convolutional neural network is a type of neural network that uses at least one of its strands to
perform convolutional operations. A convolution is an operation on two real-valued functions.
Convolutional neural networks [5,6,7] are deep learning algorithms used for image analysis and
facial recognition. The way CNN works is that it divides images into layers, where each layer
represents a complex feature. The first layer is responsible for finding simple patterns like the
contours of objects or the shapes of objects, while the inner layers try to find more complex parts like
eyes, a nose, or even the same face.
The whole process starts with a convolutional layer, where the inserted photo is divided into
smaller parts, each part passing through different filters, also known as cores. These filters help us to
capture and identify low-level properties. The convolutional layer then goes through a RELU
(Rectified Linear Unit) nonlinear activation function [5,6], which can detect more complex patterns.
And the ultimate solution comes out of the binding layer, where the layers are reduced in size and the
key information is stored, making the neural network more efficient and adaptable to the pattern
recognition problem.
These steps are repeated several times on the substrate to detect more complex patterns. Finally,
the join layer is reduced to a one-dimensional vector and goes to the solid layer where the image is
classified based on its properties and previous layers.
Figure 2. Convolutional neural networks
The proposed face recognition method includes the following Stages:
Stage 1. Reading the image in real-time
Stage 2. Face detection
Stage 3. Encoding the detected face and storing it in an array
Stage 4. The array obtained as a result of encoding is compared to the photos of students registered in
the database, which is recorded in the database by means of the array obtained as a result of the
encoding of the user's image, which occurs at the stage of registration.
4. System architecture
The lecture attendance registration system based on facial recognition is implemented in a client-
server architecture, the system software consists of two applications:
1. Facial recognition application.
2. System Administrator Application.
The functional structure of the system is presented in Figure 1:
Figure 1. Functional structure of the system
The facial recognition application registers the attendance of students and professors at lectures,
all users (students, lecturers) are obliged to go through the registration through the video camera after
the start and end of all lectures. The result is that the corresponding user is identified in the database,
attendance is registered and attendance time is counted. The mentioned face recognition approach is
implemented using opencv and face_recognition libraries.
Using the system administrator's desktop application, the database is fully managed, which means:
adding and managing faculties, adding and managing specialties by faculties, adding and managing
training courses according to specialties, adding and managing lecturers, adding and managing
students, adding and managing groups of student, changing and managing the lecture schedule based
on which the attendance at the lecture should be recorded, preparation of the attendance reports at the
lecture.
5. Results
5.1. The system software
The software of the system presented in the paper is developed on the .NET platform, and the
system database is designed in the MySql database management system. The database relationship
diagram is presented in the figure below:
Figure 1. Database relationship diagram
To consider some parts of the software's user interface, the image (Figure 3) below shows the main
form of the desktop application:
Figure 3. Main form
The image (Figure 4) below shows the form of adding and managing specialties by faculties:
Figure 4. Specialties
Below is a picture(Figure 5) of how to add and manage courses according to specialties:
Figure 5. Courses
The image (Figure 6) below shows the form of adding and managing students, through which the
registration of students is carried out, the photo is uploaded to the database along with other
necessary data during the registration of students, which is used to identify the student. The student's
image is stored in the database as an encoded array of float:
Figure 6. Students
The form (Figure 7) below provides for the creation of a lecture schedule by faculty, specialty, and
course, using which lecture attendance is counted:
Figure 7. Lecture schedule
In the image (Figure 8) below, we can see a form of attendance tracking that allows us to get
information about both student and lecturer absences. In a more specific way, we can say:
• Set a time for both the teacher and the student to arrive and leave the classroom.
• Determine the list of students in a particular course who missed the lecture.
• Determine the list of students in a particular course who attended the lecture.
• Determine how long a particular student has attended a particular course.
• Export data to PDF and XLXS files when needed.
Figure 8. Attendance form
A facial recognition application consists of a single form with a simple interface that captures a
student or lecturer with a camera, recognizes his or her face, and checks it in the database, as a result
of checking in the database, if the student or lecturer in question is identified, his or her attendance at
the lecture is registered and the time of entering the lecture is recorded in the database. Figure 9 below
shows a case of the identification of a student. Figure 10 of the below shows that the student could not
be identified.
Figure 9. Recognized face Figure 10. Unknown person
5.2. Experiment
To evaluate the efficiency of the system proposed in the paper, we experimented. We experimented on
a computer with an amd R5 processor and 16GB of RAM. At the time of experimenting, we had 30
students registered in the database. At the time of experimenting, we identified and registered 15
students. The average time it took the system to recognize each student was 0.967 milliseconds.
6. Conclusion
Within the research framework, a lecture attendance registration system based on facial recognition
has been developed, allowing us to monitor lectures attendance in real-time.
Using the system, it is possible to analyze the attendance of lecturers and students based on recorded
data.
7. References
[1] Y. K. Saheed, M. A. Hambali, A. A. Adedeji, & I. A. Adedeji. Attendance management system
using barcode identification on students' identity cards. The Pacific Journal of Science and
Technology, 17(2), 224-230, 2016.
[2] N. I. Zainal, K. A. Sidek, T. S. Gunawan, H. Manser, & M. Kartiwi. Design and development of
portable classroom attendance system based on Arduino and fingerprint biometric. In The 5th
international conference on information and communication technology for the muslim world
(ICT4M) (pp. 1-4). IEEE, 2014.
[3] S. Lukas, A. R. Mitra, R. I. Desanti, & D. Krisnadi. Student attendance system in classroom using
face recognition technique. In 2016 International Conference on Information and
Communication Technology Convergence (ICTC) (pp. 1032-1035). IEEE, 2016.
[4] A. Nuhi, A. Memeti, F. Imeri, & B. Cico. Smart attendance system using qr code. In 2020 9th
Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE, 2020.
[5] G. Hu, Y. Yang, D. Yi, J. Kittler, W. Christmas, S. Li, & T. Hospedales, When face recognition
meets with deep learning: an evaluation of convolutional neural networks for face recognition. In
Proceedings of the IEEE international conference on computer vision workshops (pp. 142-150),
2015.
[6] D. Wang, H. Yu, D. Wang, & G. Li. Face recognition system based on CNN. In 2020 International
Conference on Computer Information and Big Data Applications (CIBDA) (pp. 470-473). IEEE,
2020.
[7] Y. Zhang, D. Zhao, J. Sun, G. Zou, & W. LI. Adaptive convolutional neural network and its
application in face recognition. Neural Processing Letters, 43, 389-399, 2016.
[8] P. Taxila, Development of academic attendance monitoring system using fingerprint
identification. IJCSNS, 9(5), 164, 2009.
[9] J. Chandramohan, R. Nagarajan, T. Dineshkumar, G. Kannan, & R. Prakash. Attendance
monitoring system of students based on biometric and gps tracking system. International Journal
of Advanced engineering, Management and Science, 3(3), 239799, 2017.
[10] M. Olagunju, A. Adeniyi, & T. Oladele. Staff attendance monitoring system using fingerprint
biometrics. International Journal of Computer Applications, 179(21), 8-15, 2015.