Automated Attendance System Research Articles

Implementation of Automatic Attendance System Based on Face Recognition Using CNN Method in IPB University Vocational School Environment

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.

Automated Attendance System with GPS-Integrated with Enhanced Navigation Tracking

With the rise of automation and smart technologies in educational institutions, efficient and reliable attendancetracking methods have become increasingly essential. Traditional attendance systems, which often involve manual or barcode scanning methods, are time-consuming and prone to inaccuracies. This research paper presents an innovative approach to student attendance tracking, utilising GPS-integrated barcode identification with wireless data transmission. The proposed system is designed to improve attendance accuracy and streamline student identification without the need for students to physically interact with scanning devices. By integrating GPS and barcode systems into the door panel, this attendance solution detects student presence in classrooms, records attendance, and transmits data in real time.

BEYOND THE ROLLCALL: SMART STRATEGY FOR ATTENDANCE

Attendance tracking is a crucial aspect of educational institutions, yet the traditional manual methods are time-consuming and error-prone. This project presents an innovative solution to this problem through the development of an automated attendance system. Utilizing advanced technologies such as face detection and optical character recognition (OCR), this system aims to streamline the attendance tracking process in schools and colleges. By automatically detecting student faces and extracting roll numbers from stickers affixed to the left side of their faces, the system eliminates the need for manual intervention, thereby improving efficiency and accuracy. Additionally, a user-friendly GUI created using customtkinter in Python enhances the system's usability and accessibility. Attendance records are automatically stored in an Excel file, allowing for easy tracking and analysis by educators.

Face Recognition based Attendance System

In today’s era, facial recognition emerges as a pivotal tool, offering security, authentication, and identification benefits across various sectors. This paper introduces an automated attendance system that incorporates real-time face recognition technology. The system aims to overcome the limitations and inefficiencies associated with traditional manual attendance methods, particularly in educational institutions and workplace environments. Two approaches are outlined: one utilizing local servers and AWS cloud recognition API, and the other relying solely on AWS resources for processing. Both approaches entail constructing a database of individuals’ images and utilizing algorithms such as Haar-Cascade and Local Binary Pattern Histogram for face detection and recognition. By automating attendance through live video streams, the system mitigates issues like proxy attendance and inaccuracies. Technologies such as OpenCV, dlib, and MySQL are leveraged to streamline implementation. This integration of face recognition with cloud computing promises to revolutionize attendance tracking, offering scalability, efficiency and accountability.

Attendance Management System Using Face Recognition

In the present academic system, regular class attendance of students’ plays a significant role in performance assessment and quality monitoring. The conventional methods are practiced in most of the institutions are by calling nadata reliability. The system is developed by the integration of ubiquitous components to make a portable device for managing the student’s attendance using Face Recognition technology. An automated attendance system for human face recognition in real time background for a college to mark the attendance of their employees and students. So Smart Attendance using Face Recognition is a real world solution which comes with day to day activities of handling employees. Here multiple user faces are detected and recognized with the trained data base multiple texture-based features.

Face Recognition Based Attendance Management System

The goal of the "Face Recognition Based Attendance Management System" project is to create an automated attendance system that efficiently and reliably marks attendance using facial recognition technology. As people enter a specified region, the technology would take pictures of them and compare them to a database of people who have already registered. The system would record the person's attendance and save the data for further use after a successful identification. By completing this project, manual attendance taking—which may be laborious and error-prone—would no longer be necessary. Additionally, it would give administrators access to real-time attendance data, which would simplify the tracking of attendance and enable data-driven decision-making . Large volumes of data would be handled by the system, which would be developed using machine learning and computer vision techniques. All things considered, this project has the power to completely change how attendance is kept track of in a variety of places, including companies, colleges, and schools. It provides a very precise and effective solution that can lower expenses, save time, and boost output all around. Keyword- Attendance Management System, Face Recognition, Camera, NumPy, OpenCV.

Automated Attendance System with QR Verification

Institutions of higher learning have found a strong correlation between student engagement in class and their overall academic success. Despite this, the prevalent method for tracking student presence remains outdated and inefficient, posing a significant challenge in courses with a high number of enrollees. Traditionally, universities have relied on manual methods for managing attendance. Addressing the inefficiencies of such systems, our initiative introduces a sophisticated attendance mechanism designed to streamline the process. This system utilizes Quick Response (QR) codes as a modern solution for monitoring and documenting the presence of students in academic sessions and practical workshops across various disciplines, as discussed in this document.

Development of Automated Attendance System Using Pretrained Deep Learning Models

Abstract - Smart classroom enables better learning experience to the students and aid towards efficient campus' management. Many studies have shown positive correlation between attendance and student's performance, where the higher the attendance, the better the student's performance. Therefore, many higher learning institutions make class attendance compulsory and students' attendance are recorded. Technological solutions for an advanced attendance system such as face recognition is highly desirable. The authenticity of attendance can be ensured by using such solution. In this work, artificial intelligence based face recognition system is used for attendance recording system. The recognized face is used to confirm the presence of a student to the class. Six pretrained face recognition model are evaluated for the adoption in the system developed. The FaceNet, is adopted in this work with accuracy of more than 95%. The automation system is supported by IoT.

Design and Development of IoT-Based Automated Attendance System Using Facial Recognition

Design and Development of IoT-Based Automated Attendance System Using Facial Recognition

FRAMEWORK FOR DESIGN OF SECURITY SYSTEMS FOR MONITORING EXAMINEES AND PROCTORS DURING EXTERNAL OFFLINE EXAMINATIONS IN NIGERIA

This paper seeks to design a system for monitoring the compliance of external examination centers to set rules and regulations governing the conduct of an external offline examination. The system is designed to check impersonation of examinees, aiding and abetting by teachers and other proctors, importing and submitting answer scripts written outside the approved examination halls and finally monitoring the script submission process to prevent swapping of answer booklets after completion of the examination. The proposed system consist of biometric authentication system, automated attendance system, Closed-Circuit Television (CCTV) cameras for video recording, a script submission system (SSS), a drop box for temporal storage of scripts and human monitors to analyse the recorded videos at monitoring center of the examination board. The system is designed to work even in remote areas with unstable internet as is the case with most examination centers in rural areas in Nigeria. When implemented the system will help check illegal activities in miracle examination centers and reduce examination malpractices by both examinees and proctors drastically.

Automated Facial Recognition based Attendance System using OpenCV in Python

Face detection and recognition systems work by detecting faces present in an image or in a video frame and identifying the person in the image. In this work, we are interested in face detection to achieve an automatic attendance system. This work is implemented using Python and can be operated from any standalone device. This automated system stores the attendance records of students/employees with proper timestamps in the local Secondary Memory. All these records are stored date-wise. The implementation of an Automated Facial Recognition based Attendance System can help in identifying and verifying a person’s identity from a digital source in real-time. Accurate attendance records are very important for classroom evaluation in schools and colleges. It also helps to keep track of the attendance of the employees in different organizations. The traditional system of manual attendance tracking in institutes can result in errors and missed or duplicated entries. The adoption of the Face Recognition-based attendance system could help eliminate these problems and shortcomings of the classical manual attendance system. The proposed work is tested with different persons with different age groups and genders in real time. The system successfully identifies the proper persons with significant accuracy and records their attendance.

Streamlining Attendance Management in Education: A Web-Based System Combining Facial Recognition and QR Code Technology

Attendance tracking has long posed challenges in educational institutions due to the inefficiency and error-prone nature of traditional paper-based methods. In response, many institutions have embraced web technologies and automated attendance systems, incorporating biometrics, QR codes, barcodes, and RFID-based technologies. However, the applicability of these systems may vary across different educational settings. This paper introduces a web-based student attendance management system that combines facial recognition technology and QR codes to address the challenges associated with manual attendance tracking in a university college. The system leverages a centralized database for streamlined monitoring and auditing of attendance records, offering the flexibility to choose between face recognition and QR code attendance marking options. User acceptance tests were conducted to evaluate the system's effectiveness, and the results indicate that the proposed system greatly improves attendance tracking transparency and demonstrates high usability based on positive user ratings. Additionally, the preference for face recognition over QR code scanning was evident. Incorporating these technologies into the automated attendance system represents a substantial advancement in educational technology, offering an accurate and efficient way of recording attendance.

Face Recognition Attendance Monitoring System

Abstract: As we know attendance is the crucial part in colleges, schools, corporate offices etc. Attendance plays an important role for keeping track of student’s record. Mostly attendance is taken manually in most of the colleges and schools. The manual attendance process involves taking attendance by calling the name or by signing the attendance sheet. This manual process is very time consuming and it is difficult to maintain the attendance record. Due to this drawback of manual attendance system, there is a need of automated attendance system. Here facial recognition plays an important part for automated attendance system. This suggested study focuses on face detection algorithm to provide automated attendance system. This proposed system uses Histogram of Oriented Gradients (HOG) and Deep Convolutional Neural Network for face detection and face recognition respectively to build automated attendance system.

Automatic attendance system based on CNN–LSTM and face recognition

Automatic attendance system based on CNN–LSTM and face recognition

To Wrap, or Not to Wrap: Examining the Distinctions Between Model Implementations of Face Recognition on Mobile Devices in an Automatic Attendance System

To Wrap, or Not to Wrap: Examining the Distinctions Between Model Implementations of Face Recognition on Mobile Devices in an Automatic Attendance System

Face Recognition-Based Automatic Attendance System in a Smart Classroom

The smart classroom is a fully automated classroom where repetitive tasks, including attendance registration, are automatically performed. Due to recent advances in artificial intelligence, traditional attendance registration methods have become challenging. These methods require significant time and effort to complete the process. Therefore, researchers have sought alternative ways to accomplish attendance registration. These methods include identification cards, radio frequency, or biometric systems. However, all of these methods have faced challenges in safety, accuracy, effort, time, and cost. The development of digital image processing techniques, specifically face recognition technology, has enabled automated attendance registration. Face recognition technology is considered the most suitable for this process due to its ability to recognize multiple faces simultaneously. This study developed an integrated attendance registration system based on the YOLOv7 algorithm, which extracts features and recognizes students’ faces using a specially collected database of 31 students from Mustansiriyah University. A comparative study was conducted by applying the YOLOv7 algorithm, a machine learning algorithm, and a combined machine learning and deep learning algorithm. The proposed method achieved an accuracy of up to 100%. A comparison with previous studies demonstrated that the proposed method is promising and reliable for automating attendance registration.

RANCANGAN SISTEM ABSENSI SISWA OTOMATIS BERBASIS SMART QR CARD MENGGUNAKAN ALGORITMA BCRYPT

Various problems due to shortages in attendance are still implementing the system manual method so that the teacher feels that it has reduced flexibility in attendance. Following up on the problem, system changes must occur in order to support the attendance program. The system that was built applies some of the technology (internet of things) with a combination of hardware that functions to scan student cards of the Smart Quik Response Card type and web-based software for processing machine data with a MySQL database. The method of choice for software design is to apply bcrypt as password security at login to overcome breaches in attendance data. The results of this study built an automatic attendance system based on a smart quik response card. Based on testing by applying the blackbox method to accurately assess the attendance system that has been built, it gets a compatible score or runs smoothly for each trial feature. After experimenting with the implementation at the research location, it can be concluded that the process of absenteeism that occurs at school aims to make teachers more efficient and facilitate administration in using the application system to become disciplined and timely students.

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Traditional attendance recording methods can be replaced with artificial intelligence (AI) methods. This paper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction. The Python programming language is used to build the system, which employs object detection and feature extraction algorithms. This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detection algorithms to detect the faces in the video. The face locations are then sent to the feature extraction algorithms to extract and compare features with the features stored in the database. Our paper concludes that automating the attendance registration process using facial recognition technology can achieve up to 100% accuracy. Comparing our proposed system with previous studies, we find that it outperforms them in several aspects. Our method handles real-time tests with a larger number of students. It accommodates students positioned at different distances from the camera within the classroom, demonstrating our method's effectiveness in automating the attendance registration process.

Automatic Attendance System Using Face Recognition

The project aims to develop an automated attendance system using face recognition technology. The system eliminates manual methods and provides contactless attendance tracking. It utilizes a Raspberry Pi board, camera module, and advanced image processing techniques to detect and recognize faces in real-time. Attendance data is stored in a database which is excel sheet and can be accessed remotely. The system improves efficiency, reduces errors, and promotes hygiene and safety in various environments.

Using ECG Authentication for Biometrics in Smart Cities

All the biometric systems are based on some important features of these modalities which are mainly known for their uniqueness in one way or the other. However, the automatic attendance system using heart biometrics is focused as it is internally unique and focused on internal flexibility of the heart. Heart biometrics include different authentication modalities such as ECG, SCG, PCG and so on. It is primarily focused on authentication using ECG signals which uses algorithms or techniques such as SVM (support vector machine) for authentication purposes and dynamic time warping for signal matching. Algorithms used have shown high accuracy results and the challenges faced were considerable and effectively managed to improve for further advancements.