Automated Attendance System Research Articles

Improved Facial Recognition for Attendance Systems

Attendance is an important part of educational life. The attendance methodology which exists currently is either totally manual or requires some human assistance. In traditional methods, the teacher manually marks the attendance of each student on paper. There are many flaws in this method. As the process is manual there is a chance of error in the marking of attendance. Similarly, marking of false attendance by the students is also possible. Moreover, the attendance is then required to be updated in the online database of the institute through which it is accessible to the students. Also, after every month a report of all the students is to be generated. This overall process is cumbersome and time consuming. There also exist some automated systems for attendance like fingerprint verification and RFID but they also require some human support. Here, the automated attendance system using facial recognition plays an important role which requires no human intervention and is fully automatic. Face recognition has always remained a major focus of research because of its non-invasive nature and because it is people's primary method of person identification.

An Automated Attendance System Based on the Concept of Histogram of Oriented Gradients

This paper concentrates on an automated attendance system that makes use of face recognition to identify the person and mark attendance for that particular student. The proposed system intends to facilitate the administrators and other stake holders who are responsible for maintaining the attendance of an institution. We aim to minimise, not eliminate though, the human interactions in the process of marking attendance, which when done manually is more tedious, time consuming and includes the usage of lot of resources like papers. In addition, the use of modern technology to support, replace or reinstate the typical way of marking attendance can be seen as an advancement towards a brighter and well managed future in the field with effective work done through automation.

AUTOMATED STUDENT ATTENDANCE MONITORING SYSTEM IN CLASSROOM BASED ON CONVOLUTIONAL NEURAL NETWORKS

Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years, convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the features of building an automated student attendance system by biometric face recognition using the convolution neural network modelhas been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system: creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance monitoring system will significantly improve the organization of the educational process to ensure its quality.

Computer vision based classroom attendance management system-with speech output using LBPH algorithm

Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required.

Feature Learning Approach for Facial Recognition Using Deep Metric Learning

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

Bluetooth Smart based Automated Attendance System

Bluetooth Smart based Automated Attendance System

An Automated Attendance System in Academia with Implicit Authentication via Mobile Devices and Wi-Fi Connection

Many automated attendance systems have been proposed in academia. However, all of them require the attendees to consciously record their attendance. This is prone to human error as some attendees may forget to do that. It is also distractive as the recording process should be remembered by the attendees, in addition to focusing on their main responsibilities. This paper proposes an automated attendance system with implicit authentication, based on the connection between the attendees' mobile devices and the Wi-Fi network. Our evaluation shows that this system is almost 100\% accurate in recording staff attendance. It is applicable due to many factors. For example, many attendees often bring their mobile devices and connect the devices to the Wi-Fi.

Development of an Attendance System Based on Cloud / Fog Computing with Data Recovery Capability

Given the high importance of attendance for university students, upon which the possibility of keeping or losing their places in the course is based, it is essential to replace the inefficient manual method of attendance recording with a more efficient one. To handle this problem, technology must be introduced into this process. This paper aims to propose an automatic attendance system based on passive Radio Frequency Identification (RFID), fog, and cloud computing technologies (AASCF). The system has three sides. The first one, which is the Client-side; works on collecting the attendance data then sending a copy from it. The second side, which is the Server-side, works on calculating an absence ratio of all the students during the course. The third side is the Fog-server. Data sent by the client-side reaches to the Fog-server which, in turn, sends data to the cloud at the end of the of working time at the university. This paper also reviews the state-of-the-art automatic attendance systems and shows the merits and demerits for each approach by providing a checklist comparison. Unlike the previous works, the proposed system protects data from wasting and ensures its arrival to the cloud even in cases of connection losing or device crashing, which is the contribution of this paper.

A MULTIPLE FACE RECOGNITION SYSTEM WITH DLIB’S RESNET NETWORK USING DEEP METRIC LEARNING

Despite many approaches in facial recognition models, we often come across single facial recognition systems from an image.But the detection and recognition of a single face from an image is not very practical in an ever-changing world.For this, we need systems capable of detecting and recognizing multiple faces from a single image with which we can solve many real-world problems with a fewer number of images.So, we proposed an enhanced and efficient model, called HPMR (High Performance Multiple-face Recognition), to detect and recognize multiple faces from a single image. In this paper, we used Dlib’s ResNet network with 29 convolution layers to recognize faces. The network supports both Predictor 5 and Predictor 68 model to estimate facial landmarks. This model is trained on a data set of three million faces from well-known data sets like face scrub and VGG using deep metric learning. It uses HOG feature descriptor for multiple face detection. We used this model because it gives us better results compared to other existing models. Among many possible applications for multiple facial recognition, we have implemented an automated attendance system to demonstrate our approach.

Automated Attendance System based on Facial Recognition

Automated Attendance System based on Facial Recognition

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.

Smart Attendance Notification System using SMTP with Face Recognition

Till today attendance marking is manual event for many educational bodies. It is a mandatory, common and important activity in day to day life of a faculty member. Manual attendances maintaining is bit difficult process, time consuming effort while doing analysis or report generations on it. Few automated systems are developed to overcome those complexities. Still there are so many drawbacks like cost effective, fake generation, accuracy. To overcome these initiations, there is a need of innovative a smart and automated attendance system. This paper exactly focused on producing a secure attendance marking system which is based upon one of the human gesture as Face. There are two stages to implement the approach. One is face detection using Haar classifier. Second one is face recognition using LHBP classifiers which is generated from trained faces.The proposed system is going to record the attendance of the people who are present in a classroom environment autonomously and this is an easiest way to produce the analysis and proof oriented approach makes as reliable applications.

A Conceptual Model for Automated Attendance Marking System using Facial Recognition

The project represents an automated attendance system based on face recognition using discriminative local binary pattern and local directional pattern descriptors. The proposed system involves face detection, Features extraction and matching. The face detection is to detect faces based on Viola Jones algorithm . In feature extraction stage, the discriminative local binary pattern is used for different object texture feature extraction process. The proposed method with new features retain the contrast information of image patterns. The Facial recognition (or face recognition) is a type of biometric application that can identify a specific individual in a digital image by analyzing and comparing patterns based on the data stored in database. Smart attendance is a real time face recognition used for handling day to day activities of the employees and students. In manual attendance system there are several issues like fake attendance and mistakenly marked absent by carelessness of the faculty/teachers/lectures. In order to overcome these problems we are using this smart attendance system. Here multiple faces are detected and recognized with trained with various features. The automated face attendance marking system gives accurate performance.

Attendance Monitoring Using Face Recognition with Message Alert

Face recognition technology is the least intrusive and fastest bio-metric technology invented so for. The accurate recognition of a person is the sole aim of a face recognition system and this identification may be used for further processing. The methods to exploit this physical feature have seen a great change since the advent of image processing techniques. Here, the camera detects and recognizes the persons while them entering the door and then sends their personal information to the host to generate a 3D Facial Model. The proposed system will update the attendance once the students face is match with the template database. This paper describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment.

Design and Implementation of RFID Based Attendance System

Attendance is taken at various places like schools, colleges for students and login logout time for employees in industries and offices. The existing system is done on paper and it consumes lots of papers and it is of manual work. It has less accuracy. In order to reduce the usage of paper RFID based attendance system is used. It is an automated attendance system where Rfid tags are used. This is the easy way to take the attendance without any physical contact. In this project we use RFID Reader, RFID Tags, LCD display, which are interfaced withATMEGA8 Microcontroller. The Rfid tags are given to the students, with their roll numbers as their tags. When the tag is placed near the reader, the data is transferred to reader due to the radio frequencies. Reader then transfers data to the microcontroller. Microcontroller checks for the data continuously, if any data is received, microcontroller compares the data in data base. It eliminates the duplicate entry of data and reduces errors in time and entries.

Improved Intuitive Automated Attendance System using Unorthodox Algorithms

Improved Intuitive Automated Attendance System using Unorthodox Algorithms

An Advanced Attendance Marking system using facial Recognition

With advancement in computing and telecommunications technologies, digital images and videos are playing main role in the current information era. Human face is an important bio-metric object in image and video database of surveillance systems. Detecting and location of human faces and facial features in an image sequence is an important task in dynamic environments, such as videos where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. An automated attendance system is used for marking human face recognition in real time for college to mark the attendance for their staff and students. Smart automated attendance marking using real time face recognition is a real world solution which comes with day to day activities of handling employees. Here multiple user faces are detected and recognized with the data base trained multiple texture based notability.

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

RFID and PIR Motion Sensor based Automated Attendance System for Educational Institutions

Automated attendance system is the advancement that has taken place in the field of automation replacing traditional attendance marking activities. Designing an automated attendance system provides full transparency and maintains a regular and accurate updating of the attendance in the database. This system makes the task of attendance management easier while allowing correct attendance status and making the same data available to all concerned. This is implemented by integrating two technologies to achieve a foolproof system for effective attendance marking. This can be made feasible by synchronizing a combination of Radio Frequency Identification Detection (RFID) and Passive Infrared (PIR) motion sensor. Based on the verification the student identity, the RFID tag can be embedded in the ID card of the individual and their presence can be detected by a PIR motion sensor

Automated Attendance System

Automated Attendance System