Automated Attendance System Research Articles

Face recognition based attendance management system by using machine learning

Attendance is an important need of every organization. Maintaining daily attendance register is a tough and time consuming task. There are many alternative methods for marking the attendence like Biometric, RFID, eye detection, voice recognition, and many more. But Facial Recognition is an efficient and smart method for marking attendance. As it is known that primary identification for any human is its face, face recognition provides an accurate system which deals with the ambiguities like proxy in attendance, high cost, and time consumption. This system uses face recognizer library for facial recognition and storing attendance. It has a camera that takes an input image, an algorithm to detect a face from the input image, encode it and recognize the face and mark the attendance in a spreadsheet. The system camera of an android phone/laptop clicks the image and sends it to the server where faces are recognized from the database and attendance is calculated on basis of it. The aim of reducing the errors that occur in the older attendance marking system has been achieved by implementing the automated attendance system using deep learning. Face recognition system has been presented using deep learning which shows robustness towards recognition of the users with accuracy of 98.3% and result is converted into a PDF File. Index Terms- Android application, Biometric, Recognition system, Face Recognition, Deep Learning, PDF.

Automatic Attendance System through Facial Recognition using Deep Learning

Due to a large number of students in the schools and colleges most of the time teachers are not able to monitor on the attendance of the students. This may create the problems to both students and teachers in the end of the year or semester. The manual handling of all these can take much more effort in making the students to attend the classes regularly and also to intimate their parents on the valid interval of time. So to make this system more efficient and thereby to increase the productivity of the college or the school proposed system uses the automatic attendance system, where the proposed model takes the input from the staff regarding the attendance details and facial images of the students, then the system trains a Convolutional Neural Networks that have been deployed specifically for the facial recognition purpose. This trained model is then utilized for the purpose of achieving the identification of the student’s faces through the test image and the decision making approaches for the marking of the attendance. The methodology has been subjected to the evaluation for the accuracy of facial recognition which has resulted in highly accurate outcomes.

Attendance and Performance Monitoring Using ML

Abstract: Attendance Management is very important for every organizations. Marking and maintaining attendance is a very time consuming task. It takes lots of time to mark attendance manually. It is difficult to analyse attendance of students how frequently one is skipping classes. There will be some problems regarding proxy attendance of some students. 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 RBT levels of students' performance in the internal assessment exam and can be analysed, exactly in which type of RBT level the student is lagging behind. Keywords: Attenndance Monitoring; deep learning; convolutional neural networks (CNN), Machine Learning, paper setter, Internal Assesment Analysis, RBT(Revised Blooms Taxonomy) can be used;

Face Recognition based Automatic Attendance Updating System

The current methods for taking attendance are inconvenient and time-consuming. Manual recording allows for easy manipulation of attendance records. A smart and validated attendance system is needed to tackle this problem. In smart attendance systems, face identification, fingerprints, Genetic material, retina, eye retina, gesture recognition, and other biometrics are often used. Humans can be identified by their faces, which have distinguishing traits. Face recognition is one of the most useful image processing applications, and it plays an important part in the technical world. The recognition of a human face is a hot topic for verification reasons, particularly in the context of school attendance. A face detection and recognition attendance system uses face biostatistics known for high monitoring and other computing technologies to identify students. The purpose of creating this system is to replace the traditional technique of documenting attendance by calling names and storing pen-and-paper records with a digital version. An innovative solution for a face recognition-based automatic attendance system is presented in this study. The findings are tabulated in an excel file, and students' attendance is processed depending on the date from the excel file, and relevant conclusions are drawn from this.

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.

A REAL-TIME FACE RECOGNITION ATTENDANCE SYSTEM BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND SINGULAR VALUE DECOMPOSITION

Attendance is an essential component of every organization. Keeping an attendance register daily is a challenging and time-consuming task. There are numerous automated ways available such as Biometrics, Eye Detection, Speech Recognition, etc. for human verifications. This paper outlines a simple and effective way for tracking attendance. Face recognition provides an accurate system that solves ambiguities such as fraudulent attendance, excessive cost, and time consumption. For facial identification and attendance storage, this system employs a face recognition library in Open CV (Python). The camera captures the image and sends it to a database folder containing images, which identify faces and calculate attendance. The goal of creating this automated attendance system utilizing Artificial Intelligence was to reduce the errors that occur in the traditional attendancetaking system. A face recognition system has been presented that has robustness toward user recognition and the result is transformed into an Excel Sheet in Real-Time

Automated Attendance System using Real Time Face Recognition and MySQL Database

This project aims to design a automated attendance system using face-recognition and MySQL database. We have presented our idea to implement an “Automated Attendance System Using Real Time Face recognition and MySQL Database”. The application includes face identification, which saves time as well as being purely software based it can be flagged as eco-friendly as it reduces the use of paper and also send a message to the student of his attendance record in the end of every day. This system also eliminates the chances of fake attendance because of the face being used as a biometric for authentication. This system avoids the concept of fake attendance where attendance plays an important role. The proposed system is designed in Python as well as SQL database. The algorithm used in the system compare the image captured encoded value with the value already available with us to recognize the face. The system has output in the form of MYSQL Database.

Automated Attendance System Based on Facial Recognition using Viola-Jones Algorithm

Facial Recognition is the most used technology nowadays. Apart from Bio-metrics, Iris scan, fingerprint recognition methodologies, facial recognition is emerging recognition methodology these days. One of the most effective applications of this methodology is automated attendance using facial recognition, which is contact less, secure, and effective unlike in tradition way (manual attendance) it saves more time. Methodology used in this project involves Viola-Jones algorithm for face detection and Eigenfaces approach for feature selection and classification. In Viola-jones algorithm inputs are taken as captured images of individual persons and produce a dataset containing cropped images of individual and these dataset is directed to Eigenfaces approach as input and training of data occurs through the process of calculating eigen vectors for each eigenface. At the time of testing, Euclidean distance between eigen vectors of testing image and eigen vectors of trained eigen faces determines the matched individual. Facial recognition can also be done with PCA, which has 79.6 percent accuracy, and LBPH, which has 90.23 percent accuracy. However, when employing the Eigenfaces technique, the accuracy is 93.07 percent. MATLAB software with Computer Vision Toolbox and Deep Learning Toolbox is used for this work.

Digital Attendance System using Face Recognition Technique

Abstract: In this era of digitalization, everything is interlinked and are online. Maximum of things are using ML (Machine Learning), AI (Artificial Intelligent), IoT, Data Science etc. Making use of this, an automated attendance system can be built. So, this project is proposing “Digital Attendance System” using “Face Recognition Technique”. Entering and keeping information in database and using algorithm to extract the face features, this way face recognition technique is achieved. And this technique is used to compare the captured image of source with that of database, resulting in Digital Attendance System which can be used to mark the attendance and so the motive is achieved. Keywords: Attendance system, Face Recognition Technique, dlib library, High resolution camera.

A comparative study on radio frequency identification system and its various applications

<span>The radio frequency identification (RFID), is a wireless technology system that is used for identifying an individual or objects through the means of radio waves that transfer information from an electronic tag, called an RFID tag. RFID consists of two main components the interrogator and the transponder. The Interrogator, which is the RFID reader, the interrogator usually transmits and receives the signal while the transponder that is the tag, is attached to the object. In the RFID system, an RFID reader interrogates the RFID tags. This tag reader generates a radio frequency interrogation, which communicates with the tags been registered in the system. This reader likewise has a receiver that captures a reply signal generated from the tags and decodes the signal. This reply signal from the tags reflects the tag's information content. Each tag of the employee or student consists of a unique identity, identification card (ID) that is assigned to a single employee or student ID card, which is recorded, in the database of the system. This research reviews some recent designs and implementation of internet of things (IoT) attendance systems using the concept of the RFID system. The analysis found that the RFID system is a very advanced technology for an automatic attendance system in an institution, organization, or university and it provides a very higher performance and accuracy than the traditional paper-based system that the employees or students normally used to sign. The use of the RFID technology enables the institution, authorities, or management to evade attendance documents from damages such as misplacement, tear, or even got lost. A combination of the model is needed which will confirm higher security, better performance, and consistency of the system.</span>

다수의 얼굴 인식을 통한 자동출석체크 시스템

Non-contact biometric identification technology is once again receiving social attention these days when untact technology is required due to the Covid-19. Face recognition technology is being used in a variety of platforms such as airport immigration systems, hospital patient management systems, financial self-authentication systems, criminal suspect detection systems, and personal authentication systems for mobile devices, and is showing its effectiveness. In this paper, multiple face recognition technology is applied to the automatic attendance system. Unlike when only one person is recognized, multiple face recognition results in lower accuracy and slower processing speed. In this paper, a frontal face detection algorithm is proposed. Using the frontal face detection algorithm, only the frontal face was used for recognition, and when 36 people were recognized, 97.2% accuracy was achieved. In addition, we proposed an erasing multiple face recognition method and increased the average number of frames processed per second as the recognition completion rate increased.

A SURVEY ON AUTOMATIC ATTENDANCE SYSTEM ALONG WITH TEMPERATURE USING IOT

Automatic attendance methods are very helpful for students and workers in order to make use of their time more effectively. Maintaining attendance is the most troublesome assignment in different organizations. In recent days we have seen abrupt increment in the utilization of biometric technology in the fields of IT, education institute, transportation, etc. Internet of things is also blooming parallel. Automatic attendance system is an implementation of internet of things through Arduino ide, thingspeak finger print scanner R305 in order to reduce the time consumed by the traditional attendance method. There are various technologies like RFID, fingerprint and face recognition technologies are introduced in order to save time and reduce efforts. In this paper we are going to compare those technologies and understand which is best among them.

Attendance Recording System using Face Recognition

Image processing automated attendance system is the system in which easiest way to record the attendance for organization .This system is based on the face detection and face recognition algorithms. For this we make use of “Image Processing” using “MATLAB”. The concept of this paper is to provide real time attendance of students in a class to the faculty’s data base. Automatically detects the student using the web camera and only detect the facial part of that particular image and the image undergoes the various techniques and will compare with reference image, Later the attendance of the student is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.

Real Time Implementation of Face Recognition based Smart Attendance System

This research work addresses the issue of incorporating an automatic attendance system to the frame of an institution using face detection and recognition techniques. The proposed system aims at reducing computational time with available hardware to yield more efficient results. The proposed model utilizes Histogram Oriented Gradients and facial encodings derived from facial landmarks. It also addresses the problems related to accuracy of facial recognition and the resource requirement for quick, real-time facial recognition by applying multi-processing. The improvement in performance in terms of accuracy across two different methods, and the improvement in terms of time requirement for the same method using different strategies have also been documented for demonstration. The designed system demonstrates the effectiveness of task parallelization with a minimum amount of hardware desiderata. The system has been designed to an optimum self-sustaining ecosystem which can efficiently operate on its own accord and compute comprehensible feedback without the requirement of any third-party human interference. A Graphical User Interface has been incorporated into the system for maximum user comprehensibility

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.

Attendance Management System using Face-Recognition

Nowadays Educational institutions are concerned about regularity of student attendance. Even in pandemic situation attendance is still a major issue in schools and colleges. Mainly there are two conventional methods of marking attendance which are calling out the roll call or by taking student sign on paper. They both were more time consuming and difficult. Hence, there is a requirement of computer-based student attendance management system which will assist the faculty for maintaining attendance record automatically. In this project we have implemented the automated attendance system using ‘TKINTER’ and ‘PYTHON’. We have projected our ideas to implement an “Automated Attendance System Based on Face Recognition”. The application includes face identification, which saves time as well as being purely softwere based it can be flagged as eco-friendly as it reduces the use of paper. This system also eliminates the chances of fake attendance because of the face being used as a biometric for authentication. Hence, this system can be implemented in a field where attendance plays an important role. The proposed system is designed in TKINTER platform supported with a script of PYTHON as well as SQL database. The algorithm used in the system is based on image comparison on the basis of the encoded values of the face from the image from database with the image recorded by the system in run time. The system has output in the form of excel sheet.

A Review Paper on Facial Recognition for Automated Attendance System

A Review Paper on Facial Recognition for Automated Attendance System

Automated Attendance System and Voice Assistance using Face Recognition

In this work we propose a Live Attendance Marking System for institutional purpose. This system will enable the department to mark the attendance of students automatically by recognizing their faces. The system is based on the face detection and recognition algorithms and automatically recognizes a student whenever he/she comes across the camera module. Further after the recognition, it automatically updates his/her attendance in the Cloud web server database. The basic architecture and all the algorithms used in the model are described in the paper elaborately. Moreover in order to evaluate and enhance the performance of the system many of the filter functions are used which provide the capability to capture and recognize the images even in dull and low light places. This project also suggests the technique to ensure that a student can mark the attendance only once in a day. The live attendance system is much more efficient to traditional attendance systems both in saving time and in maintaining the database.

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.

Raspberry Pi based Intelligent Classroom Information and Management System using LBP Method

Educational organizations are gradually using multi prevention models to deal with tutorial requirements of pupils. The underpinning of those methods is that the execution of widespread practices premeditated to backing the tutorial requirements of the overwhelming widely held scholars. Investigators and experts are trying to valid measures to support teachers for effective classroom practices, which are sensitive to vary over time. Knowing the information about the classwork, students’ strength in a particular class, faculty details, passing the vital information to the students, alerting faculty with their classwork information, and automatic attendance system plays a key role in the smooth running of educational institutes or organizations. Particularly, in women colleges, passing the information about the ward to the parents and video surveillance is crucial. This paper aims at providing solutions for manual work to a great extent. The proposed system works using RaspberryPi 3 board with the display, face detection algorithm, face recognition using Local Binary Pattern method (LBP), GSM900, 4x4 Keyboard, mobile, and serial communication protocols. The new entrant to the campus will easily know about the college information and punctual working of the stakeholders in the campus. The unauthorized entry is detected using a webcam, and information is passed to the central unit for further action.