Face Recognition Based Attendance System Research Articles

Face Recognition-Based Attendance System Using Python: A Novel Approach to Automated Attendance Management

This study shows a face recognition-based attendance system created with Python. It uses machine learning algorithms to boost accuracy and productivity. Old- school attendance methods often needed to be corrected and took a lot of time. This new system changes that by using real- time facial recognition to do the job. The system relies on a mix of Logistic Regression, Support Vector Machines (SVM), and Random Forest classifiers, with a Voting Classifier to make sure it works as well as possible. The team did a lot of work to clean up facial images and fine-tune settings to make the model more accurate. The results show big improvements in managing attendance, with high accuracy and strong performance in different settings. This system offers a solution that can grow with organizations looking to simplify attendance tracking, reduce human mistakes, and make their operations more effective. Key Words: Face recognition, attendance system, machine learning, real-time application, Voting Classifier, Python.

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

Face Recognition Attendance System

Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

Automated Facial Authentication Attendance System

Abstract: This research paper introduces a novel approach to automate attendance tracking in educational institutions through the implementation of a Face Recognition-based attendance system using Python. Traditionally, attendance management has relied on manual processes, prone to errors and time-consuming activities such as roll-call or name calling. The primary objective of this project is to revolutionize attendance management by developing an automated system that utilizes facial recognition technology. By leveraging modern advancements in computer vision, this system aims to streamline the attendancetaking process, enhancing efficiency and accuracy while reducing administrative burdens.Implemented within the classroom environment, the system captures student information including name, roll number,admission number, class, department, and photographs for training purposes. Utilizing OpenCV for image extraction and processing.The workflow involves initial face detection using a Haarcascade classifier, followed by facial recognition utilizing the LBPH (Local Binary Pattern Histogram) Algorithm. Upon recognition, the system cross-references the captured data with an established dataset to automatically mark attendance. Furthermore, to facilitate easy record-keeping, an Excel sheet is dynamically generated and updated at regular intervals with attendance information, ensuring seamless integration with existing administrative processes. This research provides a practical solution for attendance management and also helps in broader discourse on leveraging emerging technologies for optimizing educational and organizational workflows

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.

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient

Face Recognition-based Attendance System

Face Recognition-based Attendance System

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient functioning of the system.

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.

Smart Face Attendance System

Smart Face Attendance System

Employee Presence System Using Nodemcu For Fingerprint And Face Id Internet Of Things (Iot)

This research aims to design and implement an Internet of Things (IoT) based attendance system. The designed system integrates two types of attendance systems, face recognition-based attendance system (FRA) and fingerprint-based attendance system (FPA), with a server. FRA uses Python programming language and Openc CV library, while FPA is developed using ESP8266 MCU Node and AS608 Fingerprint scanner with Adafruit Fingerprint library. FRA and FPA are connected to a web server with a database engine via an internet connection and send attendance data using the HTTP_POST method. The server development uses Apache Webserver, PHP programming language, and MySQL database engine. The server has two primary purposes: to record attendance data sent by FPA and FRA and to generate attendance reports based on user requests. System testing is done on a local network. The results show that the subsystems and integrated systems work well.

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.

Face Recognition-Based Attendance System for Education Institute

Facial recognition technology is a crucial aspect of human identification, making it an ideal solution for attendance monitoring in educational institutions.[2] Traditional methods of attendance taking, such as roll calls and sign-in sheets, have proven to be time-consuming and inefficient. Consequently, there is a pressing need to enhance and upgrade the current attendance system to be more user-friendly and effective. This project aims to develop a face recognition-based attendance monitoring system that overcomes the limitations of the existing system. The current system suffers from ambiguity, leading to inaccuracies and inefficiencies in attendance tracking. Furthermore, it faces challenges in enforcing attendance regulations. By utilizing face recognition technology, which leverages the unique and difficult-to-duplicate characteristics of the human face, this project aims to address these issues. The system scans the faces of newly admitted students, capturing sample images (up to 100 samples per student), which are stored for training the model to ensure accurate predictions. During attendance sessions, the student's face is compared against the stored images to verify their identity. Once identified, the system automatically records the attendance by saving the necessary information, including the attendance status, in an Excel sheet. At the end of each day, the attendance information for all individuals is compiled into an Excel sheet and emailed to the respective faculty. This paper provides insights into the functionality and implementation of this project in educational institutes, showcasing its potential to revolutionize the attendance management system

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.

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.

Face Recognition Based Attendance System

In colleges, universities, organizations, schools, and offices, taking attendance is an important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one making our task easy. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, section, and photographs, is trained and is also available for teachers and staff attendance. The images are extracted using Open CV a library of python. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

Face recognition-based attendance system with anti-spoofing, system alert, and email automation

The subject matter of the article is the design of an attendance system based on face recognition with anti-spoofing, system alarm, and Email Automation to improve accuracy and efficiency, highlighting its potential to revolutionize traditional attendance tracking methods. The administration of attendance might be a tremendous load on the authority if it is done manually. Therefore, the goal of this study is to design a reliable and efficient attendance system that can replace traditional manual approaches while also detecting and preventing spoofing attempts. Without the manual approach, attendance may be collected using many kinds of technologies, including biometric systems, radiofrequency card systems, and facial recognition systems. The face recognition attendance system stands out among the rest as a great alternative to the traditional attendance system used in offices and classrooms. The tasks to be accomplished include selecting appropriate facial detection and recognition technologies, implementing anti-spoofing measures to prevent intruders from exploiting the system, and integrating system alarms and email automation to improve accuracy and efficiency. The methods used include selecting the Haar cascade for facial detection and the LBPH algorithm for facial recognition, using DoG filtering with Haar for anti-spoofing, and implementing a speech system alarm for detecting intruders. The result of the system is a face recognition rate of 87 % and a false positive rate of 15 %. However, since the recognition rate is not 100 %, attendance will also be informed through email automation in case someone is present but is not detected by the system. In conclusion, the designed attendance system offers an effective and efficient alternative to the traditional attendance system used in offices and classrooms, providing accurate attendance records while also preventing spoofing attempts and notifying authorities of any intruders.

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.