Attendance Monitoring Research Articles

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.

Smart Attendance Automation System

The smart attendance marking system, which is based on face recognition technology, is a modern solution that may aid in the automation of the attendance management process in a variety of organizations, including schools, colleges, and corporations. Using facial recognition technology, the research team hopes to create an efficient system for tracking and recording attendance. The system will be developed by gathering and analysing data in order to construct a database of faces that will be used to train deep learning algorithms to recognise faces effectively. Compared to traditional attendance tracking systems, the smart attendance marking system based on face recognition technology has various advantages. For starters, it saves time and lowers manual labour because attendance is automatically tracked. Second, it enhances precision, lowering the possibility of errors. Third, it delivers real-time attendance updates, making it simple for instructors or administration to monitor attendance and take appropriate action. The aim of the work is expected to improve attendance management in various organisations by optimising attendance monitoring methods while decreasing manual effort and errors. Because the system may be connected with existing attendance management systems or utilised as a standalone solution, it is a versatile and adaptable option for a variety of organisations. The paper’s effort is to design, develop, and implement an efficient system that can track and record attendance accurately using facial recognition technology, giving benefits such as time savings, accuracy, real-time updates, and better security.

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

Student Attendance Monitoring System using Facial Recognition

In this age of rapidly evolving technologies and trends, performing routine tasks like Attendance Recording should no longer follow the manual or semi-manual tiresome methods. Taking students’ attendance records in the classroom during a lecture period is now a common practice in the University systems especially in most private institutions in Nigeria; a case study employed is Babcock University, Ilisan Remo, Ogun State, Nigeria. This helps to account for where a student is per time, increases student’s learning focus and helps the institution to make the right decision for instance whether to allow a student to take the final exam or not. Attendance marking using conventional methods such as calling students’ names one by one or having them to write it could be quite tedious and time wasting. It becomes more difficult to manage when the class size is large. Another challenge includes the possibility of capturing a proxy attendance such as students writing for their absent colleagues. This study proposes to solve this problem using a biometric information processing known as the facial recognition system. Its application is easier with working range larger than others such as fingerprint, iris scanning and signature. Many algorithms and techniques have been developed to improve facial recognition performance, but the proposed model employs Cascade Classifier which breaks the problem of detecting faces into multiple stages. For each stage, the algorithm performs a very rough and quick test, and if this current stage passes, it does a slightly more detailed test. It eventually detects a face if all stages are passed. The implementation tools include Python, HTML, MySQL, PyCharm, XAMP server as the local host server and a web browser to register and display results. As a result, this development makes attendance monitoring simple, efficient, and time saving. Keywords: Attendance, Monitoring, Biometric, Facial Recognition, Cascade Classifier.

Attendance monitoring of masked faces using ResNext-101

SARC virus, Coronavirus, Ebola and bird flu have all caused pandemics in the last few decades. Most of these diseases spread through the air when someone coughs, sneezes or even talks. The government makes citizens wear masks. Furthermore, all academic activities are conducted in virtual mode as a result of this predicament, making taking attendance of pupils difficult while they are wearing masks on their faces. To overcome this issue, the proposed work will track a student’s attendance while using ResNext-101 in a virtual classroom setting. ResNext-101, a deep learning technique, is used on masked faces in this work and it is a good model for accurately detecting masked faces. By using the Gaussian data augmentation approach, the outcome reveals a level of accuracy of 51.70 percent with a loss of 1.9452.

Attendance Monitoring based on Facial Recognition using LBPH algorithm

Attendance Monitoring based on Facial Recognition using LBPH algorithm

Low-Cost Active Monitoring of Attendance using Passive RFID Technology

In this paper, a smart attendance system for students attending schools is proposed. The proposed attendance system is based on Radio Frequency Identification (RFID) technology to facilitate automation and convenience. The proposed RFID Attendance System (RFID-AS) should be used by school administration to ensure safety for students as well as using it for grading and evaluation purposes. After careful study, passive RFID technology is selected to be used by the proposed system for its reasonable cost. The main components of the system are an RFID tag, an RFID reader, Visual Studio (XAF Tool), and SQL Server to compare the data from the RFID tag with the students’ database to record attendance automatically. A Graphical User Interface (GUI) is developed using Visual Studio (XAF Tool) to allow parents and school faculty to log in and browse the students’ records. Students will pass the classroom door, which will have an integrated RFID reader device to read their RFID. The paper discusses the design of the solution as well as the testing scenarios.

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

The Influence of Attendance and Exam Monitoring Card on Academic Achievement of Students

The Influence of Attendance and Exam Monitoring Card on Academic Achievement of Students

Attendance Monitoring System Using Face Recognition

Abstract: The primitive method of taking attendance through pen-paper or registers by the organizations and institutions are not much efficient these days. The proxies of the absentees by their groups or friends are much common due to which this important factor of monitoring the class becomes ineffective. The pen-paper attendance system non-essentially consumes much time in class while smart techniques gives more time to lecturer. It can be simply be manipulated. So, for that many organizations and institutions have replaced it through biometric sensors which mark attendance through fingerprints of students or staff. But due to covid-19, where touching anything is risky. As we know in the current situation during covid-19 pandemic, Government of different nations have a strict guidelines for social distancing required to be followed everywhere keeping the aspect of safety measures in mind. For that, an attendance monitoring system has been devised through face recognition. This face recognition system works in the following stages - face detection, face pre-processing, database creation, face training, face recognition, attendance maintenance. This project spotlight on the significance of the face alignment, hence how precise image is and False Acceptance Rate that can be noticed. The system processes on Face Recognition Grand Challenge (FRGC) with up-to 95% precision. Few students do mark the fake attendance of their classmates by using their digital devices specially phone through which they try to show the picture of their friend to the system but in that case it will display warning message with a beep sound. The motive behind producing face-recognition system is to save the time and make system smart and efficient. Keywords: Face Recognition, Open CV, Machine Learning, Real and fake face detection, Graphical User Interface

IoT based Attendance Monitoring System

IoT based Attendance Monitoring System

IoT based Attendance Monitoring System

IoT based Attendance Monitoring System

IoT based Attendance Monitoring System

IoT based Attendance Monitoring System

Attendance Management System Using Face Recognition

Attendance Monitoring is a common daily activity in Schools, Colleges, Workplaces and Managed Workplaces. It involves an Attendance gathering entity, a physical storage and the attendees. This task of manual Acquisition of attendance data is often tedious and hectic as well as time consuming assuming the limited Workplace time frame. Living in an age of automated machines, it is only feasible that this task be automated as well. This Project deals with using facial data of students for attendance detection systems and using a digital database to store the record with a functional User Friendly UI to be able to view it. This is achieved using A host Computer, A Raspberry Pi with camera hardware as well as a webpage to display the attendance data The data is shared between Pi and the host laptop to manage the detected faces. The data gathering is done via a trained model through the host laptop and is used via the Pi camera to detect the faces and create the digital database. The impacts of the projects will be profound since it will save time and effort of skilled individuals as well as prevent the massive paper wastage that follows manual attendance monitoring. It will also help in ease of access in the attendance of data and allowing the users to monitor their attendance themselves. This also allows for storage of massive data for longer periods of time for future use and reusing the same systems later also conserving the costs and materials.

Attendance Management System Using Facial Recognition

The main purpose of this project is to build a face recognition based attendance management system for any institution or organization to enhance and upgrade the current attendance monitoring system in to a more efficient and effective one. The current system has lot of ambiguity that causes inaccurate and inefficient attendance record. Problems of attendance monitoring and management that exists in the current system can be resolved using current emerging technologies. The technology working behind this proposed project will be the face recognition system. The human face is one of the natural traits that can uniquely identify an individual. Possibilities for a human face to change or being duplicated is low. In this project, databases of human face images will be created to use them in the recognizer algorithm. During the attendance human faces will be compared against the stored data to seek for identity. When an individual is identified, attendance will be recorded automatically by saving the necessary information in to an excel sheet database. Further the saved attendance information can be used for monitoring and management purposes.