Smart Attendance Research Articles

CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.

IoT-Integrated Smart Attendance and Attention Monitoring System For Primary and Secondary School Classroom Management

The monitoring of student attendance is a crucial aspect of the assessment of academic performance. The conventional methods for monitoring student attendance have inherent limitations in terms of both time efficiency and accuracy. Consequently, there is a clear need for a more expedient and precise attendance system. The objective of this research is to present the design of a real-time attendance recording and monitoring system for students from elementary school to senior high school, which will be implemented using the concept of the Internet of Things (IoT). The proposed system employs biometric technology in the form of face recognition. The methodology commences with the capture of images of students who leave the classroom during the instructional period via an active camera positioned on the classroom door. The system employs a Convolutional Neural Network (CNN) algorithm and a powerful computer vision tool, OpenCV, to perform real-time face recognition. Teachers will be informed of student absences and returns, as well as at the 15th and 30th minutes. An absence exceeding 30 minutes is classified as truancy. The integration of sophisticated technologies, such as machine learning and image processing, not only enhances the precision of attendance records but also equips educators with an efficient and automated system for streamlining classroom attendance management. This not only optimizes the learning environment but also facilitates more advanced and efficient pedagogical practices.

Real Time Attendance Monitoring System using Face Recognition

The management of the attendance can a huge burden on the teachers if it's done by hand. To solve this problem, a smart and auto attendance management system is being used. However, authentication is a significant issue in this system. The smart attendance system is commonly executed with the assistance of biometrics. Facial recognition is one of biometric methods to enhance this system. By using this framework, the situation of proxies and students being marked present despite their physical absence can easily be resolved. The main implementation steps used in these types of systems are facial detections and recognizing the detected faces. This paper suggests a model for implement an automated attendance management system for students of a class. The suggesting system makes the usage of Haar classifiers, OpenCV and LBPH algorithm. Following facial recognition, attendance reports will be produced and store in excel formats. The system is experimented under various conditions like illumination, head moves, the changes of distance between the student and cameras. After intensive testing, overall complexities and accuracies are calculated. The Suggested system demonstrated to be an effective and powerful device for taking attending in a classroom without any time use and manual works. Keywords – Face Recognition, Face Detection, OpenCV, Haar Classifiers.

Automatic Face Recognition Attendance System using Python and Open CV

The primary goal of this project is to develop a face recognition-based attendance monitoring system for educational institutions, aiming to improve and modernize the current attendance procedures, making them more efficient and effective. The existing system suffers from ambiguities that lead to inaccuracies and inefficiencies in attendance recording. Numerous issues arise when authorities cannot enforce the regulations of the old system. This project leverages face recognition technology to address these problems. During attendance sessions, the system will compare faces against a database to verify identities. Once an individual is identified, their attendance will be automatically recorded, with the necessary information in an Excel sheet. This automated system aids faculty in taking attendance for the entire class without causing any disruption or wasting time. We propose and have implemented a smart attendance system with the potential to incorporate Quick Response (QR) codes as a future method for managing attendance. This system aims to streamline and document student attendance during lectures and other activities across all relevant courses. At the end of the day, the Excel sheet containing attendance details for all individuals will be emailed to the respective faculty members.

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.

Smart Attendance System (Multi-Factor Authentication)

Abstract: The "SMART Attendance System using Facial and OTP Verification" project aims to revolutionize traditional attendance management processes by integrating advanced biometric technologies. In this project, a sophisticated system is developed to automate attendance tracking using facial recognition and OTP verification methods. The system utilizes computer vision algorithms to accurately identify individuals based on their facial features, ensuring reliable attendance recording. Additionally, OTP verification adds an extra layer of security, enhancing the integrity of the system. The project encompasses the design and implementation of the software and hardware components required for seamless operation. Through this innovative solution, organizations can streamline attendance monitoring, improve efficiency, and mitigate identity fraud. The project not only offers a practical application of emerging technologies but also provides valuable insights into biometric authentication systems' development and deployment.

Smart Campus: Smart Attendance Management System using Face Recognition

This Review Paper studies several research papers based on the Smart Attendance system for recording attendance based on facial recognition. Efficient and accurate attendance management is a crucial task in various educational and organizational settings. Traditional methods of manual attendance tracking are time-consuming and error-prone. The proposed system utilizes computer vision and deep learning techniques to recognize and verify the identity of individuals in a given environment. Through the deployment of facial recognition algorithms, it captures and matches facial features with existing records in a database, allowing for seamless and efficient attendance tracking.

Portable smart attendance system on Jetson Nano

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.

Smart Attendance System

Abstract: The combination of Electronics Engineering and Information Technology is played a significant roles in developing several aspects in academics such has in students attendance monitoring. Taking attendance manually often time consuming process which also increased mistakes and false in the attendance so overcome this situation we make this project Smart Attendance System. In this project, we will learn how to build IOT based Smart Attendance System using NodeMCU ESP8266 12E, OLED Display and Finger print module R608. we have also created database to register the student fingerprint in database. The system works in such a manner that when we put our finger on the sensor, which would scan their finger print and send the data to the NodeMCU.

FaceMark Pro: Smart Attendance Tracker with Liveness Detection Feature

Abstract: The goal of this project is to build a "FaceMark Pro: Smart Attendance Tracker with Liveness Detection System" in order to enhance and modernize the existing Attendance System, which was formerly marked on paper and based on cards. The current system has many problems, including inaccurate attendance records and other human error. To address the shortcomings of the previous systems, there is a solution in the form of the FaceMark Pro: Smart Attendance Tracker, which uses face recognition technology to take, store, and identify images to show individual presence. An aspect of the project involves gathering up-to-date portraits of people and organizing them into a database. Consequently, numerous recognition algorithms, such as LBPH, HaaR Classifier, HoG, and others, work as part of OpenCV while the system is in operation and a human face is recognized. We'll record everyone's attendance if any images turn up. It will first record attendance, then supply the necessary information about the participants, and last, it will deliver a sheet listing all of the names of those who are and are not present.

SmAtt: Smart Attendance System with Facial Recognition and ID Verification

SmAtt: Smart Attendance System with Facial Recognition and ID Verification

Automated face recognition system for smart attendance application using convolutional neural networks

Automated face recognition system for smart attendance application using convolutional neural networks

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.

Web-based smart attendance monitoring system using QR code and GSM module for SMS notificatio

The study was designed and developed to address certain problems and concerns of the school administration and parents of students attending Ciriaco Mariano Elementary School. The system was used to provide the school an innovative way to record attendance of students, provide parents with real time updates regarding the entry and exit of their children in the school premises, and be able to monitor students by generating attendance reports. The Web-based Smart Attendance Monitoring System using QR code and GSM module for SMS notification was developed through the implementation of the Iterative System Development Life Cycle Model. This iterative model ensures that the web-based system conformed to the all of the requirements from the CMES and of the requirements set by the ISO 25010 criteria. To assess the quality of the developed web-based system, the researcher adapted the ISO 25010 Software Quality Standard as evaluation tool. Two sets of questionnaires were developed and were given to respondents who were selected using purposive sampling. The first set of questionnaires were given to IT experts to assess the technical qualities of the system which includes functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. The second set of questionnaires were given to the school principal, teachers, and parents to assess the web-based system quality on selected criteria which includes functional suitability, performance efficiency, and usability. The Web-based Smart Attendance Monitoring System using QR code and GSM module for SMS notification was successfully developed using the iterative SDLC model and found conformed to the ISO 25010 software quality standards as implied by the results from the evaluation of IT experts, school principal, teachers, and parents.

Design and Implementation Proximity Based IoT for Smart Attendance System

The reporting of course attendance is an essential component of academic administration. This research designs and deploys a smart attendance system by leveraging proximity-based IoT. The smart attendance system is built upon the Android operating system to detect the beacon of the proximity sensor and is integrated into a database installed on a cloud server. The application supports three different features designated for administrators, lecturers, and students, respectively. The developed application accelerates data input for the campus administrator, records the course log efficiently, and concurrently reports the more secure student attendance from manipulation since it applies a single user identity. This study conducts a field test to evaluate the operational efficacy of the system by sampling students who enrolled in the course. The test involves utilizing a range of mobile devices to access the application connected to two courses. In one course, the functionality testing result is 91.67%, while in another, it is 100% successful. The limitation arises as a result of the incapability of the mobile device to perceive the beacon.

Assessing Functionality and Usability of Smart Attendance Apps for Student Monitoring System

Assessing Functionality and Usability of Smart Attendance Apps for Student Monitoring System

Smart Attendace System Using ML

Abstract: The world is changing, and automatic face recognition (AFR) technologies have advanced greatly. Smart Attendance with Real-Time Face Recognition is a practical method for managing the student attendance system on a daily basis. The technique of recognising a student's face for collecting attendance using facial biometrics based on high-definition monitor footage and other information technology is known as a face recognition-based attendance system. In my face recognition project, a computer system will be able to quickly and accurately find and recognise human faces in pictures or videos being taken using a security camera. Many algorithms and methods have been created to enhance facial recognition ability, however deep learning is the idea that will be used in this case. It assists in turning the video frames into photos so that the student's face can be quickly identified for their attendance and the attendance database can be quickly updated automatically.

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

QR- based Advance Attendance System Using 2-Step Authentication

bstract: Student participation in the classroom has a direct impact on their academic success in higher education institutions. The majority of student attendance registration is still done in the traditional manner, which is cumbersome and timeconsuming, especially for courses with a significant number of students.The majority of institutions have historically handled attendance control by hand. The goal of this paper is to encourage the potential use of the Quick Response (QR) code as a future attendance management system, to track and record student attendance in lectures and exercises for all pertinent courses, in order to resolve the manual attendance issues. We proposed and implemented a smart attendance system to do this.

A Smart Attendance System Based on Face Recognition: Challenges and Effects

Modern high technology has significantly advanced thanks to the fourth industrial revolution, and artificial intelligence has made tremendous strides. Facial recognition is one of the most important computer vision jobs in real life, with applications ranging from intelligent services to security and attendance systems