Recognition Based Attendance System Research Articles

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.

Facial Recognition Based Attendance System with Cloud-Integrated Data Management

Managing attendance at a place where number of people are more is a challenging task, and likely there are few percent chances of error always there. To solve this problem in this paper we have proposed our Face-recognition based attendance system with cloud integrated data management. Another important thing that is having the record of attendance we have used cloud database to store and record attendance. The database is automatically updated by system developed by PyMongo, so that later that can be also displayed to user via their login onto a dashboard. In this project we have built face recognizer with OpenCV python and Eigenfaces algorithm which uses Principal Component Analysis (PCA) and used MongoDB Atlas, a service of Mongo for hosting the cloud database. We will be using MERN stack for building dashboard on which one can monitor their attendance. Using OpenCV against MATLAB also helps in saving memory and gives higher speed of execution

Survey paper on Face Recognition Based Attendance System

The integration of face recognition technology into attendance management systems has emerged as a transformative solution for monitoring and optimizing attendance processes in educational institutions and organizations. This report presents an in-depth analysis of a project dedicated to the development and implementation of a Face Recognition Attendance System (FRAS) and examines its efficiency and security in various real-world scenarios. The report begins by discussing the significance of attendance tracking systems in educational and corporate environments, highlighting the shortcomings of traditional methods and the potential benefits of face recognition technology. It explores the underlying principles of face recognition, focusing on the algorithms and techniques used to capture and process facial data. The project methodology and implementation details are outlined, covering the hardware and software components used to create the FRAS. Special emphasis is placed on the challenges encountered during the system’s development and the strategies employed to overcome them. A comprehensive evaluation of the system’s performance and efficiency is presented, including accuracy, speed, and scalability, with comparisons to traditional attendance systems. Moreover, the report addresses security concerns associated with the FRAS, such as data protection, privacy, and vulnerability to spoofing. The report concludes with a discussion of the practical implications and future prospects of implementing face recognition technology in attendance systems. It highlights the system’s potential to streamline administrative tasks, reduce errors, and enhance security, while also emphasizing the need for ongoing research and development to address emerging challenges. Overall, this report provides a valuable insight into the evolving landscape of attendance management and the role that face recognition technology can play in revolutionizing these processes, balancing efficiency with security considerations.

Face Recognition Based Attendance System Using Haar Cascade Algorithm with Histogram Equalization and Median Blur Filter

The attendance system is one of the mandatory activities in teaching and learning activities in an educational environment. Unfortunately, many still use a manual attendance system, which is inefficient, and fraud often occurs by manipulating attendance data. In this research, a face recognition-based attendance system was created. The face detection and recognition system use the haar cascade method with image preprocessing, namely histogram equalization and median blur filter. This system can provide output in the form of CSV documents containing attendance data that has been done in real time so that attendance data recording becomes more efficient than before. There is a significant difference in face recognition without and with image preprocessing. Without image preprocessing, the average face recognition accuracy rate is 71.2%. In face recognition with image preprocessing, the face recognition accuracy rate is 91.5%. Therefore, the use of image preprocessing can improve image quality and significantly increase the accuracy of face recognition. In addition, the CSV document is successfully generated automatically after the attendance process is carried out, which is equipped with user data, attendance date, and time when the user makes attendance to avoid fraud in the form of manipulation of attendance data by users.

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.

Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.

Attendance System using Haar Cascade Approach

Abstract: The participation framework is utilized to track and screen whether an understudy goes to a class. There are various kinds of participation frameworks like Biometric-based, Radio recurrence card-based, face acknowledgment based and old paper-based participation framework. Out of them every one of the, a Face acknowledgment based participation framework is safer and efficient. There are a few examination papers zeroing in on just the acknowledgment pace of understudies.. This research focusing on a face recognition based attendance system with getting a less false-positive rate using a threshold to confidence We used Haar cascade for face detection of their strength and LBPH calculation for face acknowledgment. It is hearty against monotonic dark scale changes. Situations, for example, face acknowledgment rate, misleading positive rate for that and bogus positive rate with and without involving an edge in recognizing obscure people are considered to assess our framework. We got face acknowledgment pace of understudies is 77% and its bogus positive rate is 28%. This system is seeing students regardless, when students are wearing glasses or grown a beard development . Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its bogus positive rate is 14% and 30% with and without applying edge separately.

Face recognition based attendance system using machine learning with location identification

The paper argues that maintaining regular attendance is crucial for student success, and traditional attendance management methods can be inefficient and time-consuming for teachers and administrators. For example, calling out student names or taking manual attendance on paper can take up valuable classroom time and can be prone to errors or manipulation .To address these issues, the paper suggests that a computer-based attendance management system using Computer Vision technology can be an effective solution. Computer Vision involves the use of cameras, sensors, and algorithms to identify and analyze visual data, including images of individuals. In the context of attendance management, Computer Vision can be used to capture images of students during class and automatically recognize and mark their attendance using facial recognition technology. This approach can offer several advantages over traditional attendance methods. Firstly, it can be faster and more accurate, reducing the time and effort needed to manage attendance manually. Secondly, it can provide real-time updates on attendance status, allowing teachers to track students who arrive late or leave early. Finally, it can generate reports on attendance patterns, allowing administrators to identify and address issues related to student attendance and engagement. Overall, the paper highlights the potential benefits of using a computer-based attendance management system using Computer Vision, emphasizing its ability to streamline attendance management and improve student outcomes.

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

Automated Face Recognition based Attendance System using RetinaFace and FaceNet

Automated Face Recognition based Attendance System using RetinaFace and FaceNet

Masked Face Recognition Attendance System Using A Modified Convolutional Neural Network

In this paper, a masked face recognition based attendance system is developed by modifying a version of convolutional neural network (CNN). In this regard, a Support Vector Machine is integrated in the CNN to replace its original Softmax classifier to perform the task. The performance of the modified CNN in recognizing masked faces in a 5-fold cross validation was compared that of other CNNs. The experimental results show high effectiveness of the proposed CNN (i.e. 98.92%) in recognizing masked faces for recording attendance.

A Smart Digital Attendance Monitoring System for Academic Institution Using Machine Learning Techniques

Face Recognition is the one of the foremost emerging and interesting technology. In this fast-evolving generation, attendance is usually a problem among school community and so as business community. One among the foremost efficient solution for this problem is that the Digital Attendance System. Now a days Computers plays a vital role in many fields. Likewise, in this field also, we will make an efficient use of it. Face detection and recognition-based attendance system may be a process of identifying face of the scholars using image of a face on the idea of high - definition monitor video and uploading the attendance of every student automatically, rather than manual attendance uploading process. In our face recognition Research work, a smart system is going to be ready to capture a frame from a video and process it accurately and predict which of the scholar is present therein class. There are number of algorithms and programme to detect the face of an individual, but the concept to be implemented here is Neural Networks. We used Neural Networks because, it will be used for the extraction of the frames from the video and convert it into images in so the face of the scholar will be often identified and updated automatically within the database.

Face Recognition based Attendance System

Human face is one of the natural traits and crucial part of human body that can uniquely identify an individual. In the current old system the roll numbers are called out by the teachers and their presence or absence is marked accordingly which is time consuming and has a lot of ambiguity that caused inaccuracy and inefficiency of attendance marking. The productive time of the class can be utilized very efficiently by implementing automated attendance system. The main purpose of this project is to build a face recognition-based attendance monitoring system for any educational institution or organization where attendance marking is the demanding task. It enhances and upgrades the current attendance system into more efficient and effective as compared to before. This attendance system which uses HaarCascade a machine learning Object Detection Algorithm used to identify faces in an image or a real time video, Local Binary Pattern Histogram (LBPH) a face recognizer algorithm used to extract features and compare by using python programming and OpenCV libraries saves time and efficiently identifies and eliminates the chances of proxy attendance. This model integrates a camera that captures an input image and training database is created by training the system with the faces of the authorized students.

RANCANG BANGUN SISTEM PRESENSI BERBASIS IOT

The purpose of this research is to design and implement an attendance system based on internet of things (IoT) . The proposed system integrated two types of attendance systems, face recognition based attendance system (FRA) and fingerprint-based attendance system (FPA), with a central server. The FRA was developed in a Raspberry Pi mini-computer using Python programming language and Openc CV library. The FPA, on the other hand, was developed using Node MCU ESP8266 and Fingerprint scanner AS608 with Adafruit Fingerprint library. Both FRA and FPA are connected to a web server with a database engine through the internet connection and sensing attendance data using the HTTP_POST method. The server was developed using Apache Webserver, PHP programming language, and MySQL database engine. The server serves two main purposes, which are to record the attendance data sent by the FPA and FRA, and generate an attendance report based on the user query. The system testing was done in a local network. The result showed that both the subsystems and the integrated system worked well

A Smart Attendance System for Imam Abdulrahman Bin Faisal University Using Facial Recognition

Students’ attendance is one of the methods used to ensure students success by making sure that a student does not miss many classes. The most dominant method used nowadays in the Saudi universities such as, Imam Abdulrahman Bin Faisal University (IAU), is the traditional method where the instructors are the ones responsible for taking student’s attendance. Developing new and effective methods is critical to improve the traditional way and minimise their errors. A proposed facial recognition based attendance system is one efficient method that can substitute the traditional methods. Installing cameras in classrooms where it detects faces and recognise each face for attendance registration is more competent. The system can also track students’ attendance and warn them if they exceeded the permitted absences rate. The proposed system facilitates the attendance registration process and reduces the time and effort for both students and instructors.

Face Recognition based Attendance System

Face Recognition based Attendance System

Automatic Attendance System with Face Recognition using Machine Learning

Object and Face detection and recognition is one of the mostly researched area in computer vision. This particular field of work is widely used in mobile phones and laptops for unlocking the system by the user. Recently this field gained importance in the automatic attendance system in schools, colleges and institution. The institutions are moving from biometric based attendance to face recognition based attendance system. In this project work, I have used machine learning techniques to create a complete system of automatic attendance system which can be implemented very easily. There are majorly four steps involved in the system. Firstly, the datasets can be created instantly using webcam and in the second stage the created data set have to be trained and the trainer algorithm will create the trainer.yml document. As a next step, the face recognition algorithm have to be performed in order to recognize the faces of various students and teacher. In the final step, the attendance of the students will be updated in the CSV file or Excel. The proposed work is very much suited for the real time applications like automatic attendance system. HaarCascade is very effective technique to produce much simple and accurate system.

A Survey on Face Recognition Based Attendance System and Its Techniques

A Survey on Face Recognition Based Attendance System and Its Techniques

Face Recognition Based Attendance System in Schools and Organisations

To give programmed understudy attendance framework and understudy attendance framework by actualizing a superior face discovery and acknowledgment framework. The framework takes pictures of understudies and distinguishes, examines and perceive faces utilizing picture preparing calculations. Haar course classifier and LBPH calculations are utilized for facial acknowledgment. In the wake of gathering picture handling information, the framework will create a record of definite attendance and transfer it to the cloud. In a particular time (ex: 14 minutes after class has begun) framework will send the email warnings to the understudies in the event that they are missing. After conclusive report is transferred tally of the understudies’ present will be spared. On the off chance that check is diminished during address hours, at that point email will be sent to the regarded staff about the bunking of the understudy. OpenCV library will be utilized for actualizing Haar course classifier and LBPH, Python and python library Numpy will be utilized for investigating information [10]. Area Convolutional Neural Network (R-CNN) is utilized for flawlessly checking the quantity of understudies present inside the study hall and recognizing on the off chance that somebody bunks the class.

Face Recognition Based Attendance System with Auto Alert to Guardian using Call and SMS

Face Recognition Based Attendance System with Auto Alert to Guardian using Call and SMS