Development of a Lecture Attendance Registration System Based on Facial Recognition*

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

• eol>Facial recognition
• Attendance system
• lecture
• students

1. Introduction

2. Current approaches to recording attendance at lectures

Different approaches are used to record attendance at lectures, including:

 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.

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 clientserver 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:

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:

To consider some parts of the software's user interface, the image (Figure 3) below shows the main form of the desktop application: The image (Figure 4) below shows the form of adding and managing specialties by faculties: Below is a picture(Figure 5) of how to add and manage courses according to specialties:

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:

The form (Figure 7) below provides for the creation of a lecture schedule by faculty, specialty, and course, using which lecture attendance is counted:

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.

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. 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

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 .