Proxy Attendance Research Articles

AI based College Surveillance System for Class Skipper

In many of the educational institutions, managing attendance of students/candidates is tedious, as there would be large number of students in the class and keeping track of all is onerous. There are situations where student act as proxies for their friends even though they are not present. The presence of students repeatedly skipping classes and spending considerable time wandering on campus signals potential underlying issues, such as disengagement, personal challenges, or dissatisfaction with the educational experience. Traditional methods of monitoring attendance are often inadequate in addressing these nuanced challenges. Therefore, there is a need for an AI-based College Surveillance System using Faster R-CNN to accurately detect class skippers and provide insights into their behavioural patterns. In this system, a database containing the trained student’s face. A camera installed in the college campus captures the face of all the student in the classroom and other places too. This face image is processed using FRCNN algorithms to detect faces and to mark the attendance automatically in an excel sheet. The system records the entire class session and identifies when the students pay attention in the classroom, and then reports to the facilities and also this system can record violations of classroom, that is absence, roaming around the college campus during the class hours and send alert message to the H.O.D.This dynamic attendance system uses face recognition as an important aspect of taking attendance which saves time and proxy attendance and is avoided. The system identifies faces very fast needing only 100 milliseconds to one frame and obtaining a high accuracy. Our face recognition model has an accuracy rate of 98.87%..

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.

Student Face Recognition and Alert Rule

Abstract: Attendance marking in a classroom during a lecture is not only an onerous task but also a time-consuming one at. Due to an unusually high number of students present during the lecture, there will always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of challenge. The growing need for efficient and automatic techniques for marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radiofrequency Identification tags, etc., However, these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time-saving, simple, and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to the existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, and Updating of Attendance in the database. Index Term – Mongo DB, React JS, Node JS, Express JS, Attendance, Facial recognition, and detection, Haar cascade, Alert, Student Face.

Real Time Snap Attendence

Abstract: Traditional attendance methods, such as manual roll calls and sign-in sheets, are often time-consuming, prone to errors, and ineffective in preventing proxy attendance. Face recognition technology offers a promising solution to these challenges by providing an accurate and efficient way to mark attendance. This paper presents Snap Attendance, a facial recognition-based attendance management system that utilizes deep learning algorithms to identify and verify student identities. Snap Attendance streamlines the attendance process, reduces administrative burden, and enhances attendance accuracy.

Facial Recognition Attendance System Using Python

Attendance marking in a school room all through a lecture isn't simplest a difficult project but additionally a time eating one at that. because of an surprisingly high variety of college students present all through the lecture there'll always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of project. The developing need of efficient and automatic techniques of marking attendance is a growing venture within the place of face recognition. In current years, the hassle of automatic attendance marking has been broadly addressed through the usage of standard biometrics like fingerprint and Radio frequency identity tags and so on., however, these strategies lack the detail of reliability. in this proposed mission an automated attendance marking and management machine is proposed by making use of face detection and popularity algorithms. instead of the use of the conventional methods, this proposed system pursuits to increase an automatic machine that statistics the pupil's attendance by way of the use of facial popularity technology. the principle goal of this paintings is to make the attendance marking and management machine green, time saving, easy and smooth. right here faces might be recognized the usage of face popularity algorithms. The processed photo will then be compared towards the present stored document after which attendance is marked inside the database therefore. compared to present system traditional attendance marking device, this machine reduces the workload of people. This proposed gadget can be applied with 4 levels inclusive of image taking pictures, Segmentation of institution image and Face Detection, Face evaluation and popularity, Updating of Attendance in database

Streamlined Workforce Management System Using AI

In today's digital landscape, face recognition technology has emerged as a versatile and non-invasive tool used across sectors, despite its lower accuracy compared to iris or fingerprint recognition. Its applications range from security and authentication to optimizing attendance systems in educational institutions and workplaces. This paper introduces an innovative class attendance system employing face recognition technology, replacing manual processes prone to inaccuracies like proxy attendance. The system involves database creation from student images, utilizing Haar-Cascade classifiers for face detection, and Local Binary Pattern Histogram algorithms for live recognition from classroom video streams. It automates attendance data distribution to faculty members, enhancing efficiency and precision. Simultaneously, the security industry necessitates alert and vigilant security personnel to prevent breaches. A sophisticated hybrid system, leveraging machine learning techniques like OpenCV and EAR/MAR algorithms alongside UHD cameras, detects guard drowsiness and alerts them promptly using sound cues and preventive measures, ensuring continual vigilance. This innovative hybrid system fortifies security guard surveillance, mitigating potential vulnerabilities due to lapses in attentiveness. Key Word: Face Recognition Technology, Biometrics, Attendance Management, Security, Authentication, Surveillance, Machine Learning, Open CV, Security Guard Monitoring, Vigilance, Drowsiness Detection.

Network Based Smart Attendance System for Students

In the era of rapidly advancing technology, an effective and reliable attendance monitoring system is quite vital within the learning environment. The conventional attendance monitoring strategies are not only laborious and time-consuming; they also result in mistakes made by humans and proxy attendance. With the mobile phone, which is easily accessible, a better way for attendance management can be achieved. This paper proposes a Network-Based Smart Attendance System, wherein students' mobile devices and the institutional WiFi or local network can be used to facilitate the attendance process. In this digital solution, cases of attendance by proxy and errors in recording will fall drastically. More importantly, the record-keeping is burdensome to the faculty, which can be reduced, making the attendance system manageable and efficient to handle. Additionally, this solution could be integrated with machine learning, IoT devices, and NFC technology to further enhance its functionality and effectiveness. In this paper, a design of the system along with its implementation will be presented that details how it can improve the entire attendance management procedure in an educational institution.

Intelligent Face Recognition Based Students’ Attendance System

This paper aims at developing an intelligent face recognition students’ attendance system to enhance school attendance tracking. The system is made up of four phases- database of students’ details, face detection, face recognition and attendance report. The database stores all the students’ details and images of the students’ face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required.

Automated School Bus Attendance System

Abstract: In our education world, the most common way to mark the attendance is to sign or by call the student. This traditional way of marking attendance takes longer period of time and is also problematic in several ways this include proxy attendance. To eradicate this problem in this technological world we introduce “Face Detection Attendance System”. In this biometric system, Attendance will be taken by using faces of student. This system is faster than our traditional way and also reduces the possibilities for proxy attendance.it is also tasted under various conditions like illumination, head movement, the variation of distance between the student and camera. This system will be installed in the school buses which will then keep the track of every student. While detecting the face of student, System compares the image of the test and the training image. Attendance data is stored in an excel sheet. Attendance data is stored with time date and day for future reference.

The development of a face recognition-based mobile application for student attendance recording

Attendance-taking has been practised in most learning institutions, schools, colleges, and universities to monitor the students’ commitment towards their studies. The traditional way of attendance-taking is usually performed by passing a piece of paper to be signed by all the students in the class. The main drawback is proxy attendance, whereby a student can sign on behalf of their friend. Students frequently miss classes are at a higher risk of missing important information, discussions, demonstrations, and assignments, leading to knowledge gaps and hindering their overall academic progress. Additionally, traditional attendance-taking takes significant time for the paper to circulate among the students, especially in larger classrooms. This can result in wasted instructional time and disrupt the class flow. The students might be distracted, and their focus on the class material might also be disrupted. Regarding record management, paper-based attendance records can easily get misplaced, damaged, or lost. Retrieving and analysing attendance data becomes time-consuming when papers must be manually sorted. Not only that, considering the post-Covid pandemic nowadays, everyone is still expected to minimise physical contact to reduce the possibility of infection. This, however, is quite impossible if manual attendance-taking is practised because everyone needs to touch the same paper to sign the attendance. Therefore, in this project, a face recognition-based mobile application is proposed to facilitate attendance-taking while minimising the drawbacks of the traditional attendance-taking approach. The main content of this article will be based on the phases involved in the development process. The output interface of the proposed system will be presented from both lecturers’ and students’ views. Finally, this paper is ended with the system limitations and recommendations for future work.

A Smart Attendance Capturing Mobile App

Abstract: Keeping track of attendance while engaging students in the classroom may be tough, especially when the class is big. The conventional method of calling pupils' names is tedious and time-consuming, and proxy attendance is always a possibility. To address this problem and maintain track of students' attendance, we presented a smart attendance management system (SAMS) using face recognition, and location.This system replaces the traditional method with a mobile application which eventually reduces the use of pen and paper. The student needs to install the application on the mobile. the application will work as a user interface that can be accessed by Teachers, Students, and admins.Students can view their overall attendance using face recognition and their statistical data is also presented in the application. Each student can be tracked using a unique user ID and the student's presence can be recognized by using GPS coordinates typically longitudes and latitudes.

Evaluation of Self-Reported professionalism lapses among dental undergraduate students: A cross-sectional study from a Malaysian dental faculty.

Professionalism and academic integrity are important components of dental education as they influence not just the present-day learning process, but also the practice of dentistry in future. This study evaluated self-reported professional lapses, academic dishonesty behaviours among peers and recommended sanctions for such lapses. Dundee Polyprofessionalism Inventory I: Academic Integrity questionnaire was administered to BDS students of a private dental institution in Malaysia. Differences in the level of recommended sanctions were assessed by Mann-Whitney U and Kruskal-Wallis test. There was unanimous agreement that all 34 statements of lapses of academic integrity as unacceptable. The highest agreement (95.6%) was related to threatening or abusing university employees or students and involvement in paedophilic activities, whereas the lowest agreement was observed for getting or giving help for coursework against rules (47.3%). The most frequent behaviours observed among peers were lack of class punctuality (55.1%) and providing and receiving proxy attendance services (49.3%). About 36% admitted to not being punctual themselves, 26.8% for accepting or providing help for course work and 22.9% for receiving and providing proxy attendance. Female students displayed stricter recommended sanctions, with the most significant difference relating to joking disrespectfully about body parts (p < .001). More lenient recommended sanctions were observed among Year 1 students compared with other years, with the most significant difference (p < .001) observed for exchanging information about the exam, forging a signature, plagiarising and cut-pasting materials without acknowledgement. The majority of academic lapses were well comprehended. Significant differences were observed in sanctions imposed in terms of gender and year of study.

Face-Nest, Facial Recognition Attendance System with Timestamp Logs: An Information System Security Approach

In the ever-expanding world of technological advancement and development, many new concepts and technologies are being presented to the world that aim to streamline current processes or innovate an approach to an existing problem. The pandemic has brought forward new operating conditions that have forced many to make the leap to go fully digital, including the education sector. Therefore, this conducted study aims to develop facial recognition attendance system that promotes convenience and accurate attendance marking. This is crucial to ensure time taken to record students’ attendance could be minimize without any sacrifice in accuracy, and theoretically limit the occurrence of proxy attendance within the education setting. Quantitative research was conducted in Malaysia, which involved 82 students from private university. This is crucial to ensure students are able to engage in the teaching and learning process, and at the same time develop a meaningful learning environment. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) software. Research findings show that the facial recognition attendance system contribute to improve the quality of education within online settings by limiting the occurrence of attendance proxying among students. Therefore, this finding contributes by providing a research direction for improving attendance in education setting by utilizing facial recognition attendance system.

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.

Modern Attendance System - A New Way For Taking Attendance by Face Recognition

In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advan- tages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and problematic to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. Faces are detected and recognized from a camera placed inside the classroom.

ATTENDANCE MONITORING SYSTEM

To maintain a discipline and let students grasp utmost knowledge in schools, colleges and universities the attendance system was introduced. There are two conventional techniques to mark attendance of students in a particular class. One of them is by calling the roll number and the second is to take students sign on a piece of paper against their roll number. Hence there was a need to evolve this system in such a way that it could become user friendly, less time consuming and efficient. This is an automated system to assist the faculty in taking attendance of the whole class without any disturbance or time waste. The idea can encompass a large number application one of which include face identification, it will help save time and efficiently identifies and eliminates the chances of proxy attendance. The main purpose of this project is to built automated attendance system using Raspberry pi 3B+ with OpenCV/Python libraries and recognizer algorithm have been implemented. The proposed system can be implemented in any field where attendance system is present and plays a vital role. In addition, as the project objectives and the design criteria all met, it’s greatest to say this project is an engineering solution for all university and colleges to track and manage the attendance.

Attendance and Performance Monitoring System Implementation

Abstract: Attendance Management is very important for every organization. Taking attendance of lot student is very time consuming process. It is difficult to analyze attendance of students how frequently one is skipping classes. And also there will be some proxy attendance of student in traditional system. The possible solution to this problem is to use an automatic attendance system which uses face recognition techniques. This system will mark attendance electronically and recorded attendance will be stored in a database. The preparation of a question paper for the Internal Assessment Exam can be automatically generated with the help of teachers according to RBT levels. The Internal Assessment is very important for students. This system will give individual student reports based on the performance of student in internal assessment, exactly in which type of RBT level the student need to improve themselves.

IoT based Student Attendance Management System using RFID and Face Recognition

Attendance is for keeping records of number of students present in schools, colleges or in any organization. It is very important constituent in maintaining discipline among students in acollege and imparting quality education in schools, colleges. Complexity of this process increases even more with increase innumber of students. Biometric student attendance system increases the efficiency of the process of taking student attendance.Automatic wireless identification has been facilitated by IoT technology using electronic passive and active tags with suitable readers. In this paper the concept of a simple and portable approach of IoT is applied to student attendance that records the attendance using RFID and Face Recognition. This system aims to automate the awkward process of manually taking and storing student attendance records. It will increase the reliability of attendance records by preventing proxy attendance. As per the requirement by the teacher, the securely stored attendance records can be reliably retrieved. This concept is capable of eliminating time consumption during manual collection of attendance and an opportunity for the educational administrators to capture face-to-face classroom data for allocation of proper attendance scores and for further managerial decisions.

Student Attendance System via Face Recognition

Abstract: When attendance is done manually, it takes a lot of effort and time. Student attendance is a part of educational institute which is most challenging part of the virtual platform. There was also a lot of proxy attendance due to lack of a good attendance system. The face is the most important part of human body which identifies a human. In this paper, we describe an efficient algorithm of haar cascade using an open-source image processing framework known as OpenCV. Therefore, in this paper, we work on an attendance system via a face detection algorithm in real-time using the frontal face recognition concept.

Attendance: An Application Based on Facial Recognition

Abstract: Attendance is an important aspect to record the presence of students. The presence of someone is a way to address that the student is carrying out their obligations to come. Usually, attendance is done manually using pen-paper. This digital era has brought forth new technologies that can help deal with this absence of students in organisations. Automatic Face Recognition (AFR) technologies have made many improvements as well as advancements in the field of biometrics. It uses RealTime Face Recognition as a solution to keep a record of the attendance. The Face recognition approach has proven to be the best of all Biometrical sources of identification. We propose a face recognition desktop application that will automatically update a student's attendance into the database on the web server, providing an ease to the faculty. Even students can know their attendance just by a login. A report based on student’s attendance can help in evaluating the attendance eligibility criteria. In this face recognition desktop application, we use Machine Learning and Deep Learning algorithms to capture multiple images of students to reduce human errors and proxy attendance. This desktop application is much more efficient and safer than the traditional pen paper method. Keywords: Attendance system, Automated attendance, Image Processing, Face detection, Feature matching, Face recognition.