Classroom Attendance System Research Articles

Real Time Recognition of Face Covered with Facemask using CNN for Application in Classroom Attendance System

Because each person has a different face identity, facial recognition relies on the distinctness of individual facial traits. However, the recent Covid-19 pandemic has substantially altered daily living, required the usage of facemasks while walking outside, including for educational activities such as attending lectures at schools and universities. Facemasks' widespread use presents a significant problem to facial recognition systems since they obscure a significant area of the face, limiting the availability of important facial clues. In response to this critical issue, this project aims to create a Convolutional Neural Network (CNN) architecture suitable for use in a classroom attendance system. The research process begins with the creation of a large training dataset that includes five different students with four different facial circumstances, including facemask covering. The best CNN design is determined through painstaking testing to enhance accuracy during both the training and validation phases. The empirical findings demonstrated that the finalized CNN architecture has a remarkable validation accuracy of up to 96.2%. The trained system demonstrated outstanding precision in recognizing persons during real-time recognition tests. This experiment highlights the CNN's ability to recognize students with high accuracy even while they are wearing facemasks, indicating its potential in solving the issues provided by the prevailing public health measures.

Wired Fingerprint-Based Classroom Attendance System for Secured Student Attendance Archiving Using Arduino UNO Microcontroller

This study, which successfully addresses the shortcomings of traditional attendance-checking methods, such as human error, that are inevitable in manual attendance systems given the fact that it is time-consuming. Paper-based systems can be susceptible to forgery, as students may attempt to sign in on behalf of absent classmates. This undermines the integrity of attendance records. Introduces a fingerprint-based classroom attendance system designed using the Arduino Uno microcontroller. The research explores the feasibility of fingerprint biometrics for identity verification in educational settings. Using Arduino Uno, Fingerprint Sensor, RTC Module, and the LCD Monitor the researchers successfully developed a working prototype for the Wired Fingerprint-Based Classroom Attendance. 600 tests were applied to collect the (1.0) lowest and (2.0) highest time of the fingerprint sensor and calculate its average (1.7). The developed system operates offline, storing data securely on an SD card, making it particularly suitable for institutions in areas with restricted internet access. Comparative performance evaluations against conventional pen-and-paper methods highlight the fingerprint-based system's notable capacity, accuracy, positioning it as a transformative tool to enhance attendance tracking procedures and eliminates attendance-related issues to improve overall classroom operations.

A Literature Review on the Application of Ibeacon in Education Settings

With the advent of iOS 7, a new technology from Apple called iBeacon appeared, iBeacon made a significant step forward in the Internet of Things (IoT) (Townsend, 2023). As technology is becoming a more central part of people’s lives, people’s devices start to talk to each other more and iBeacon helps a device figure out what is nearby and what it can talk to. Currently, iBeacon is also widely applied in education settings, which promotes heated discussion on its potential in education sector application (Spachos & Mackey, 2018), many publications have also been issued in the academia on the application of iBeacon in education settings, but so far, there has not been any review on these studies. This article presents a literature review of the studies on the application of iBeacon in education settings published from 2016 to 2020, aiming to discuss the widespread application of iBeacon in educational contexts, and shed light on its innovative potential and revolutionary potential; the discussions on using iBeacon in smart green building courses, context-aware ubiquitous learning environments, flipped classroom learning, and smart campus classroom attendance system show that iBeacon can efficaciously support teachers’ instructional activities and students’ learning activities, help teachers quickly obtain students' learning situation in classroom and then may adjust their instructional approaches or contents, and help pupils effectually engage with their daily learning and develop their learning skills.

An evaluation of the RSSI Indoor Positioning Integrated Classroom Attendance System

An evaluation of the RSSI Indoor Positioning Integrated Classroom Attendance System

Smart Classroom Attendance System Using Face Recognition and Raspberry Pi

Abstract: Face detection in real-time is a component of an automated face recognition system that is also employed in the development of a separate research subject. As a result, there are a variety of ways for dealing with face detection issues. The goal of our proposed project is to detect and recognize faces, as well as to record attendance for each face. The camera connected to Raspberry pi USB port will capture the faces of students in real-time who are available in the class for face detection using Viola Jones Algorithm. The image which is captured is compared with stored images in database and then recognize the faces of every student using LBPH and according to that, attendance will be given to that class for that particular student. The details of the students are stored in MySQL database. The proposed project is being presented as a solution for the Manual Attendance Marking System. It is designed to be efficient and reliable.

Construction and Application of Automatic Attendance Prediction System for Classroom Education Teaching Based on Random Matrix Theory

Random matrix theory, as a theoretical tool for statistical processing of complex systems and high-dimensional data, has been more and more combined with big data technology and applied to automatic attendance prediction analysis of classroom education. Firstly, the basic definition of the random matrix is introduced. Secondly, the single ring theorem of the random matrix is introduced. Based on this, the random matrix model of data is established. However, few undergraduate or graduate students in physics have been exposed to this theory. By introducing the basic concept of random matrix theory, this paper analyzes the necessity and feasibility of introducing random matrix theory into automatic attendance prediction teaching in classroom education. As a biometrics technology, random matrix theory technology has been widely used in various fields of the identity verification system, and classroom attendance system is an important field of random matrix theory technology application. The traditional random matrix theory class attendance has the problem of slow recognition speed and low accuracy. With the popularity of deep learning, the random matrix theory based on deep learning has gradually replaced the traditional random matrix theory. In this paper, the SSD target detection algorithm, NB algorithm, and RF algorithm based on deep learning are used to improve and optimize the traditional random matrix theory class attendance system, which effectively improves the efficiency and accuracy of random matrix theory attendance. Then, aiming at the low recall rate and recognition rate of random matrix theory in low-pixel images, we try to apply the random matrix theory of single image resolution reconstruction method to face detection and random matrix theory and give a calculation method to deal with outliers and noise.

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

Research on Classroom Attendance System Based on Face Recognition

Deep learning face recognition technology has become one of the most popular technologies. The explosive growth in the use of face recognition has brought a variety of practical needs. Face recognition can be applied to identity authentication, bank security, forensic investigation, face scanning payment, access control system and so on. Existing face recognition methods have achieved remarkable results. With the continuous development of deep learning technology, many depth methods show better accuracy than human recognition. Based on the in-depth study and discussion of the deep learning method, this paper applies the theory to practice, and designs and develops a set of face recognition system for classroom attendance.

Increasing the Security of RFID-based Classroom Attendance System with Shamir Secret Share

<p>This paper proposes an attendance system for increasing the security based on Shamir Secret Share algorithm. The use of RFID devices for classroom attendance is still vulnerable to certain attacks. Students usually make use of existing loopholes for prohibited things, such as forged attendance. Shamir Secret Share is a security method based on the Secure Multiparty Computation (SMC) concept. The SMC guarantees not only the confidentiality of external attacks but also of each member in the secure system. In the attendance scenario using Shamir Secret Share, a student and a lecturer must do tapping at the same time; otherwise, the secret that opens the lock for attendance at that class will not be opened. To realize this system, this paper uses two RFID modules, each of which is connected to one nodeMCU microcontroller. Both systems are connected to a database where the Shamir Shared Secret calculation is performed. Some experiment has been implemented for proving the concept. The result shows that some scenarios of fraud in RFID based attendance can be prevented.</p>

Fully Automated Classroom Attendance System

<p class="0abstract">This paper is based on study and implementation of the pilot project for customized, automated, highly secured class room management system known as JIC CMS (Jubail Industrial College Class room management System). In this research paper, the strategies, design, development and implementation of a pilot project for class room attendance management systems is explained in detail. As a pilot project, mobile app is built on Android platform using Xamarin programming. Prototype of proposed system provides high level of authentication by embedding face recognition and biometric verification together with RFID system. Though given model features are specially designed for our college needs, but this model can be modified for general usage and can be applied for all academic levels i.e. school, college, universities. Among the other features of system, one important feature is selection of system to work in smart mode in which system closes after predefined time and uploaded automatically to the web server.</p>