Face Recognition Based Attendance System Research Articles

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.

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.

Development of Face Recognition-Based Attendance System

Lecture attendance management is usually tedious, and time-consuming and may be prone to errors or manipulations when done manually. Hence, in this work, a web application called RollCall was developed and tested, for use by both students and lecturers in the Faculty of Engineering at the University of Ilorin as a model to manage attendance. The system manages attendance by allowing the lecturers to create courses, and take and retrieve attendance records for the courses created. Student functionalities include uploading their face data, enrolling for courses, and retrieving attendance records for the courses in which they enrolled. Attendance is marked through face recognition technology implemented with Python, OpenCV and Sci-kit Learn. The web interface was implemented using HTML5, Twitter Bootstrap CSS framework and JavaScript. The findings revealed that RollCall effectively streamlined attendance management, making it easier for both students and lecturers to keep track of attendance records. The web application is user-friendly and enhances the overall academic experience by improving the accuracy and efficiency of attendance management

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.

Algorithmic Analysis of Automatic Attendance System Using Facial Recognition

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.