Attendance Monitoring System Research Articles

The Application of LoRa Module and Smart Card for A Large-Scale Area Attendance Monitoring System

The Application of LoRa Module and Smart Card for A Large-Scale Area Attendance Monitoring System

Enhanced Attendance Monitoring: Utilizing QR Code for Online Attendance with Laravel Framework and SMS Notification

This paper presents an attendance monitoring system that utilizes QR codes for online tracking. Developed on the Laravel framework, the system offers an efficient and user-friendly solution for attendance management. By scanning QR codes, students can easily check-in, eliminating manual roll calls. The system also includes SMS notifications for prompt attendance status updates. Thorough testing ensures system functionality and security. Overall, this system represents a significant advancement in attendance tracking, enhancing efficiency and communication for students, instructors, and administrators

Face Recognition-Based Attendance System for Education Institute

Facial recognition technology is a crucial aspect of human identification, making it an ideal solution for attendance monitoring in educational institutions.[2] Traditional methods of attendance taking, such as roll calls and sign-in sheets, have proven to be time-consuming and inefficient. Consequently, there is a pressing need to enhance and upgrade the current attendance system to be more user-friendly and effective. This project aims to develop a face recognition-based attendance monitoring system that overcomes the limitations of the existing system. The current system suffers from ambiguity, leading to inaccuracies and inefficiencies in attendance tracking. Furthermore, it faces challenges in enforcing attendance regulations. By utilizing face recognition technology, which leverages the unique and difficult-to-duplicate characteristics of the human face, this project aims to address these issues. The system scans the faces of newly admitted students, capturing sample images (up to 100 samples per student), which are stored for training the model to ensure accurate predictions. During attendance sessions, the student's face is compared against the stored images to verify their identity. Once identified, the system automatically records the attendance by saving the necessary information, including the attendance status, in an Excel sheet. At the end of each day, the attendance information for all individuals is compiled into an Excel sheet and emailed to the respective faculty. This paper provides insights into the functionality and implementation of this project in educational institutes, showcasing its potential to revolutionize the attendance management system

Development and Implementation of a Contactless School Attendance Monitoring System with Thermal Scanning during the COVID-19 Pandemic

Development and Implementation of a Contactless School Attendance Monitoring System with Thermal Scanning during the COVID-19 Pandemic

A Comparative Study of Face Recognition Models for Smart Attendance

Abstract: The Smart Attendance Monitoring System using Face Recognition is a project that aims to automate the process of tracking attendance in an organization using facial recognition technology. The system uses a camera to capture images of individuals, which are then compared to a database of pre-registered faces. The system recognizes the individuals and records their attendance automatically. The project involves several stages, including data collection, face detection etc. The system uses machine learning algorithms to analyze the images and identify the individuals accurately. The system can also generate reports and statistics, allowing the organization to monitor attendance trends and identify areas for improvement. Overall, the Smart Attendance Monitoring System using Face Recognition is an innovative solution that streamlines attendance tracking processes and enhances organizational efficiency

Students Attendance Monitoring & Accessing Control Based Raspberry - PI

Abstract: This project is "IOT based facial recognition attendance monitoring system". In order to maintain student attendance records, this project focuses on facial detection and radio frequency identification cards for an attendance recorder system. In class databases, student faces are already preserved. If the image from the database and the one taken by the raspberry pi camera match, the student's attendance will be tracked over time. Additionally, RF ID cards are utilized to regulate entry in certain areas. The ARM1176JZF-S core is used by the credit card-sized Raspberry Pi single-board computer. System on a Chip is a technique for packing all the circuitry required to run a computer onto a single chip. An operating system is required for Raspberry Pi to launch. In order to cut costs, the Raspberry Pi does not have any on-board non-volatile memory, which is typically used to hold file systems, Linux kernels, and boot loaders in more conventional embedded systems. To accomplish this, an SD/MMC card slot is included. the application-specific boot load after

An Automatic Student Attendance Monitoring System Using an Integrated HAAR Cascade with CNN for Face Recognition with Mask

An Automatic Student Attendance Monitoring System Using an Integrated HAAR Cascade with CNN for Face Recognition with Mask

Attendance Monitoring system using face recognition

Attendance Monitoring system using face recognition

Internet of Things - based Smart Attendance Monitoring System

Internet of Things - based Smart Attendance Monitoring System

IoT and AI Based Student’s Attendance Monitoring System to Mitigate the Dropout in Non-boarding Secondary Schools of Rwanda: A Case Study of Wisdom School Musanze

Purpose: This project aimed to test an IoT and AI based system that monitor students from home to schools, during class hours and from school to home and notify parents and school administrators about the irregularity observed to their respective children.
 Methodology: In this project, secondary data was used and was retrieved from the school’s record of Wisdom School Musanze located in Musanze District. The main data to consider were sex whether male or female. Another important data was orphanage,whether pupil is orphan or not orphan, and school fees payment by checking whether student paid school fees or had not paid. These mentioned data were taken randomly from senior one (S1) to senior six (S6) in academic year 2020-2021.
 Findings: The system is equipped of a finger print sensor to register and verify students and staff attendance, a Passive Infrared (PIR) sensor to detect the presence of human to wake-up the device, a real time clock to synchronize each generated report with the local time. A web application is developed to allow students real-time monitoring for parents and school administrators and the system is be able to generate a daily, monthly and annually report. 
 Unique contribution to theory, practice and policy: Classification machine learning with decision-tree algorithm is used to analyze data and generate a model to evaluate the impact of monitoring attendance on preventing students to dropout. The generated model with accuracy of 91.4% shows that keeping students’ attendance at high percentage would reduce significantly the dropout rate in secondary schools of Rwanda. 

Attendance Monitoring System Design Based on Face Segmentation and Recognition

Aim: The proposed work aim was to monitor real-time attendance using face recognition in every institutional sector. It is one of the key concerns in every organization. Background: Nowadays, most organizations spend a lot of time marking attendance for a large number of individuals manually. Many technologies like Radio Frequency Identification (RFID) and biometric systems are introduced to overcome the manual attendance system. when using these RFID and biometric people need to stand in queue to make their presence. Objective: The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems. Methods: The proposed work was done through face recognition using Machine Learning. Face recognition is a part of biometric characteristic of a human. It was easy to process than other biometric measurements like fingerprint, iris scan, hand scan, retina scan. The Haarcascade classifier will detect a face, and the LBPH algorithm will recognize the face. The experiment performs on the creation of real-time face data. Results: Using the web camera connected to the computer, face detection and recognition are performed, and recognized faces mark as attended. Here, the admin module and teacher modules are implemented with different functionalities to monitor attendance. Conclusion: Experiment results get 94.5% accuracy in face detection and 98.5% accuracy in face recognition by using the Haarcascade classifier and LBPH algorithm. This application system will be simple to implement, accurate, and efficient in monitoring attendance in real-time.

Smart Attendance System Using OpenCV

-The essential part of the human body that distinguishes an individual is their face. The primary objective of this project is to develop an attendance monitoring system based on facial recognition for academic system in order to upgrade and improve the current attendance management system and increase its effectiveness and efficient than it existed previously. With an OpenCV Real-time Face Recognition based attendance system, you may not only capture attendances but also provide security when the system needs to identify a specific student’s identity and the moment of their admission. Since it is entirely automated and much simpler, it is simple to handle the records that will be transformed into an excel file and to keep track of the students or visitors who are present. The goal of this study was to use OpenCV to create a Real-time face recognition system for school attendance that would gather information about students who were present before or after class. The system will need student registration and a password, automatically record each student’s attendance using face recognition, and transform that data into an excel or text file. Key Words: OpenCV, Real-time face recognition

Fraud Mitigation in Attendance Monitoring Systems using Dynamic QR Code, Geofencing and IMEI Technologies

Fraud Mitigation in Attendance Monitoring Systems using Dynamic QR Code, Geofencing and IMEI Technologies

Anti-spoofing-enabled Contactless Attendance Monitoring System in the COVID-19 Pandemic

The Global Novel Coronavirus Disease-2019 (COVID-19) pandemic has forced social distancing norms that have been followed worldwide. Thus, traditional biometric-based attendance marking systems are replaced with contactless attendance marking schemes. However, there are limitations of manufacturing cost, spoofing attacks, and security vulnerabilities. Thus, the paper proposes a contactless camera-based attendance system with the equipped functionalities of anti-spoofing. The proposed scheme can detect liveliness, so fake attendance marking is eliminated. The proposed scheme is also scalable and cost-effective, with generic solutions adaptable to schools, colleges, or other places where attendance is required. The system also eliminates the limitation of one-entry by multiple face-marking systems that allow simultaneous attendance marking. In performance analysis, parameters like image precision, storage cost, retrieval latency, and analysis of the anti-spoofing module is presented against existing schemes. An accuracy of 95.85% is reported for the model, with a significant improvement of 33.52% in storage cost through the Firebase database, which outperforms existing state-of-the-art schemes.

DEVELOPMENT OF EMPLOYEE ATTENDANCE AND MANAGEMENT SYSTEM USING QUICK RESPONSE (QR) CODE IN SORSOGON STATE UNIVERSITY, CASTILLA CAMPUS, PHILIPPINES

This paper aimed to develop an Attendance System using a QR code which provided an easier and more efficient way of managing the employees’ attendance records. This is a system in which the generated report was generated on a QR code. This study utilized the Rational Unified Process (RUP) method, a software engineering process that was designed with the aim of providing real-time analysis of development projects, to analyze the processes of the system. The phases - inception, elaboration, construction and transition - were also utilized in the development process of the system. A descriptive method was also employed to evaluate the system. QR Codes was generated and assigned to each of the employees of Sorsogon State University, Castilla Campus, who have alternative work-from-home schedule. Each of the employees scanned into the system the assigned QR code in order to confirm their attendance. Findings revealed that retrieval of basic information of employees, creating of travel orders, automation of computation of available leave credits and utilizing attendance using QR codes and generation of reports became more accessible, cost-effective and reduced the occurrences of errors. Also, based on the evaluation conducted, QR code as an attendance monitoring system generally met the acceptable expectation in terms of functionality, reliability, efficiency, accuracy, usability, maintainability and portability. It was concluded that the developed system was a user-friendly, efficient, cost-effective and adaptable solution for keeping track of employees’ attendance through contactless transactions without sacrificing the reliability and accuracy of the attendance reports.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0307/a.php" alt="Hit counter" /></p>

Timekeeping and Immediate Monitoring of Employees by Consistently Advocating Time Consciousness and Honesty Using Enhanced Attendance Monitoring System (TIME CATCH Using EAMS)

Attendance management methods that use QR codes and face recognition technology to identify and verify an individual’s features are widely used in many aspects of people’s lives nowadays, notably in pandemic situations where contact-less systems are used. In this paper, the development of enhanced attendance monitoring system using was introduced. The Isabela State University Angadanan Campus has implemented a biometric attendance monitoring system. However, it is limited to the number of employees’ registrations, leave management and generation of Daily Time Record. The biometric system can only store 60 employees in every device, leave application and credits cannot be catered by the system and the Daily Time Record is not auto-generated and auto-formatted. For this reason, an enhanced attendance monitoring system was developed. The system uses a camera and QR Code reader to take attendance electronically, and the attendance records be saved in a database. It can store multiple number of employees, process leave application, manage remaining leave credits, and provide auto-generated Daily Time Record. This method, on the other hand, reduces the requirement for fixed materials and employees to retain records. From the developed system and the gathered data, it has addressed the problem of the conventional way of checking employee’s attendance. The general mean of 4.72 shows that the software quality of the system based on ISO 25010 is found to be functional that the users have strongly agreed on the different characteristics of the system as it addresses the issues in the current attendance monitoring system of ISU.

Intelligent attendance monitoring system with spatio-temporal human action recognition.

This paper proposes an intelligent attendance monitoring system based on spatio-temporal human action recognition, which includes human skeleton gait recognition, multi-action body silhouette recognition and face recognition. Our system solves several problems, for example, when a mask is worn to conceal the face, which leads to a decrease in recognition accuracy performance, and when a 3D face mask is used to fake an identity. The skeleton gait feature of our intelligent attendance monitoring system uses a temporal weighted K-nearest neighbours algorithm to train the recognition model and carry out identification, while the multi-action body silhouette feature uses a multiple K-nearest neighbours algorithm to train the recognition model, identify the person and vote on the outcome. Using the proposed system, which integrates skeleton gait features, action silhouette features and face features, more effective recognition can be achieved. When the system encounters a situation with feature masking, such as when an individual is wearing a mask or has changed their clothes, or when the viewing angle is masked, it can continue to deliver good recognition ability through multi-angle skeleton synthesis gait recognition. Our experimental results show that the recognition accuracy of the system is 83.33% when a specific person wears a mask and passes through a monitored area. The intelligent attendance monitoring system uses a LINE messaging API as the access control notification function and provides a responsive web platform that allows managers to perform follow-up management and monitoring.

Attendance Monitoring System using CNN

Uniqueness or individuality of an individual is his/her face. Attendance check plays an important role in classroom management. Checking attendance by calling names or passing around a sign- in sheet is time-consuming, inefficient and especially the latter is open to easy fraud. The main aim of this project is to improvise the traditional systems and introduce a new approach using Deep Learning techniques that presents the detailed implementation of a real-time attendance monitoring system. We first capture an image of students in a classroom and utilize the OpenCV module to detect and frame the faces in that image. In the next stage we enhance these frames using an image enhancing model. In the final stage of the project, we build a Convolution Neural Network (CNN) to train these facial images and compare them with the student records that are stored in the database and hence update the attendance status of the students. Our system promises easy to maintain hassle free attendance system and with other integrations it can be used for other necessities of industries and is not limited to educational institutions.

AI based contactless attendance monitoring and management system

The traditional methods of attendance marking need to be replaced with an Artificial Intelligence based attendance monitoring system. The process of checking attendance using the existing methods is time consuming and open to easy fraud. The usage of Bio-metrics poses the threat of viruses. A contact-less Attendance Management System (CAMS) is proposed to overcome the above challenges. AI based attendance monitoring system will record the students attendance automatically while they enter the class, by recognizing their faces. The in-time and out-time of the students are also recorded on a regular basis. The Holistic Face Matching algorithm is used to compare the facial images. The total time duration, the students were present inside the class can be easily calculated. There is no chance of fake attendance as each student has to record his or her facial identity. The main objective is to achieve reliability, accessibility and security in a very efficient way. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored automatically. Finally, the student’s attendance details can be updated in the academic management system.

Attendance Monitoring Systems for an Organization

Attendance Monitoring Systems for an Organization