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Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In human interactions, the face is the most important factor as it contains important information about a person or individual. All humans have the ability to recognise individuals from their faces. Now following system is based on face recognition to maintain the attendance record of students. The daily attendance of students is recorded subject wise which is stored already by the administrator. As the time for corresponding subject arrives the system automatically starts taking snaps and then apply face detection and recognition technique to the given image and the recognize students are marked as present and their attendance update with corresponding time and subject id. We have used deep learning techniques to develop this system, histogram of oriented gradient method is used to detect faces in images and deep learning method is used to compute and compare facial feature of students to recognize them.

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

Automatic Facial Recognition Attendance System is a type of automated identification system that can recognize any person whose facial features have been saved in the database. This technology could be used by all corporations in the coming years, offices to keep track of who comes and goes. The attendance method is based on facial recognition technology. A real-time, contactless attendance tracking system that is extremely useful in today's world circumstances of a pandemic. After COVID, the work environment will not be the same. Despite the fact that the virus is still spreading, firms are attempting to restore on-premise operations in order to assure business continuity. Employees' health and safety are of utmost importance in such situations. Organizations are looking for methods to provide employees with a COVID-free workspace, and a touchless check-in is the first step. The attendance system uses a set of techniques like Haarcascade classifier and Local Binary Pattern Histogram(LBPH) Face Recognizer in deep learning to detect people in front of the camera and then changes their attendance in the attendance sheet automatically.

To Maintain the attendance record with day to day activities is a challenging task. The conventional method of calling name of each student is time consuming and there is always a chance of proxy attendance. The smart attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. There are many biometric processes, in that face recognition is the best method. Here we are using the computer vision which is a field of deep learning that is used for the camera reading and writing and using TkInter to create a GUI application.

This paper presents the Radio-Frequency Identification (RFID) based attendance management system for the employees of a company, institute, or organization. This system is developed for a daily purpose attendance recording system in every organization. In every corporate organization or company, a proper attendance management system plays a vital role in managing the salaries of employees. As we are all aware that proper maintaining of attendance will help the organization to track the regularity and sincerity of employees towards their occupation. And now in this digital world maintaining attendance manually using pen and paper has become outmoded. In earlier days, when the employees gave their attendance using pen and paper then discrepancies occurred in the entries by employees and for this during the time of entry even if the employee was late they are not marked accordingly, and there led to the improper evaluation of attendance. The time comes when the management analyses the record of each employee’s attendance and there arises a big issue with date and time and that particular employee’s attendance can be analyzed over the month. To overcome these shortcomings we had developed a system that not only removes the tedious task for tracking attendance manually but also helps in maintaining a system of records that will also help the organization with the proper and appropriate evaluation of salary, regularity, and even punctuality of an employee’s attendance. The main purpose of developing RFID based attendance management system is to computerize the attendance of each employee in an organized manner. Each employee of that organization will be having their ID card and the backside of the ID card is having an RFID tag and as employee scan their id card while entering, then their necessary details will automatically get stored in the database and which can be then computed.

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.

With the advent of technologies and multiple options available, it’s very essential to rethink and optimize the current manual process by utilizing technologies, like web, mobile together. At present mobile phones play a vital role in our day-to-day life, and it also became a mode of identity. Using the mobile and its geo tagging feature we can precisely locate and validate once presence. The inbuilt ability of mobile can be very well leveraged to modernize the current manual attendance system. QR Code scanning is one of such features of mobile phones that enables them to quickly capture and process the data. Smartphones can be used to speed up the process of taking attendance. This project ‘Multichannel Attendance Management system using QR Code and Location’ focuses on simplifying & solving the problem of manual process of taking attendance. The project proposes a system that is based on using a QR code. QR stands for ‘Quick Response’. They can be read quickly by a cell phone. It stores information such as the lecture number, date, name of the professor, etc, which is used in this proposed system.

Attendance of every student is important irrespective of the fact that the student has attended a particular session or not . Many automatic systems through which attendance can be recorded have been available such as fingerprint scanner, retina scanner, individual person taking attendance which is the manual method, etc. But the present times are a situation of pandemic caused to COVID - 19. Most of these methods are time-consuming and have expensive instruments to handle it. Also, they occupy a certain amount of storage space in the system which may be difficult to organize things in efficient manner. This also comes with the risk of spreading COVID-19 virus as many people will make use of this. The system of attendance that we propose in this paper deals with attendance management system integrated with the details of students. It generates the attendance only if the student is present during the session of the scheduled class or any meeting. Daily maintenance of attendance is mandatory for reaching a given percentage by the campus or institution. Firstly, for the attendance of a student to be captured, the student has to login into Microsoft Teams (as we are restricting this paper only to use of Microsoft Teams) for attending the scheduled class or meeting. Once the student is present in the class, the time duration of the student present in the class will be recorded. The student has to meet the certain amount of time to have his attendance to be marked with the status as present. The criteria are that the student should be present for 35 minutes at least to get the attendance marked in ERP. Keywords: Attendance, Management, System, Present, Absent, Automation / Automated, ERP, MS Teams, Institution, Algorithm / Algorithm’s. Title: ATTENDANCE MANAGEMENT SYSTEM Author: Vismitha P Y International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 10, Issue 3, July 2022 - September 2022 Page No: 1-4 Research Publish Journals Website: www.researchpublish.com Published Date: 02-July-2022 DOI: https://doi.org/10.5281/zenodo.6790165 Paper Download Link (Source) https://www.researchpublish.com/papers/attendance-management-system, International Journal of Computer Science and Information Technology Research, ISSN 2348-1196 (print), ISSN 2348-120X (online), Research Publish Journals, Website: www.researchpublish.com, {"references":["[1]\thttps://www.researchgate.net/publication/323511629_Student_Attendance_Management_System","[2]\thttps://ieeexplore.ieee.org/document/7124938/","[3]\thttp://ijcsi.org/papers/IJCSI-9-4-1-146-150.pdf","[4]\thttps://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article","[5]\thttps://www.kdnuggets.com/2017/10/top-10-machine-learning-algorithms-beginners.html","[6]\thttps://www.freeprojectz.com/php-mysql-project/attendance-management-system","[7]\thttps://www.jigsawacademy.com/popular-regression-algorithms-ml/","[8]\thttps://www.academia.edu/43552788/Monitoring_Student_Attendance_Using_A_Smart_System_at_Taif_University","[9]\thttps://www.academia.edu/39720251/STUDENT_MANAGEMENT_SYSTEM","[10]\thttp://www.jetir.org/view?paper=JETIR1602010","[11]\thttps://towardsai.net/p/machine-learning/fully-explained-decision-tree-classification-with-python","[12]\thttps://www.slideshare.net/PoojaMaan1/attendance-management-report-2016#:~:text=ABSTRACT%20Student%20attendance%20management%20system%20deals%20with%20the,username%20%26%20password%20to%20make%20the%20student%E2%80%99s%20status.","[13]\thttps://pub.towardsai.net/fully-explained-svm-classification-with-python-eda124997bcd","[14]\thttps://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm"]}

Face Recognition is a currently developing technology with multiple real- life applications. The goal of this Thesis is to develop a complete Face Recognition system. The system uses Convolutional Neural Networks in order to extract relevant facial features. These features allow to compare faces between them in an efficient way. The system can be trained to recognize a set of people, and to learn in an on-line way, by integrating the new people. Face recognition system is one of the biometric information process its applicability is easier and working range is larger than others .The face recognition is live acquired images without any application field in mind .process utilized in the system are White Balance correction ,skin like region segmentation .facial feature extraction and face image extraction on a face Candidate .The face one of the easiest ways to distinguish the individual identify each other .Face recognition is a personal identification system that uses personal characteristics of a person to identify the person’s identify. KEYWORDS:-Face Recognition, Facial Attendance, Automatic Attendance, Face Detection.

RESUMEN: En este Trabajo Fin de Grado se ha desarrollado un sistema de control de asistencia, como pro ducto para una empresa del sector de las TIC (Tecnologías de la Información y la Comunicación) en Cantabria. Sus clientes son mayoritariamente empresas pertenecientes al sector industrial, que abarcan desde pequeños talleres con apenas unos pocos empleados, hasta grandes fábricas con centenares de ellos. Previamente a la implementación de este software, en muchas de estas empresas las entradas y salidas del personal se registraban en papel o a través de sistemas cerrados, los cuales no facilitaban el acceso a los datos o la integración con sistemas de terceros. Bajo esta situación, las empresas no podían saber de manera rápida y fiable el grado de absentismo entre sus trabajadores, ni generar los informes necesarios para cumplir con el Real Decreto de 2019 sobre el control horario laboral; sin tener que sacrificar un tiempo excesivo recopilando datos en papel y procesando tediosas hojas de cálculo. Esto provocaba no solo la pérdida de tiempo y la pérdida económica asociada a la mala gestión; sino también constantes litigios con los empleados por los desajustes en el registro de sus tiempos de trabajo. El sistema presentado en este proyecto, proporciona las herramientas necesarias para corregir estos problemas. Por un lado, se ha desarrollado una aplicación móvil para dispositivos Android, que permite fichar a los empleados independientemente del entorno en el que trabajen o de su situación. Se recogen los fichajes en el momento, y se sincronizan en cuanto existe una conexión activa en el dispositivo; lo cual permite no solo fichar a los empleados de una oficina; sino también permite recoger los fichajes de operarios que trabajen fuera de las instalaciones, lo que se conoce como trabajos en campo. Por otro lado, se ha desarrollado una API REST para ser ejecutada en un servidor de aplicaciones, y a través de ella los clientes (cómo la aplicación móvil descrita anteriormente, o cualquier otro, como puede ser una aplicación web), pueden conectarse e interactuar con ella a través de la funcionalidad que proporciona. Es la encargada de almacenar y recuperar la información demandada de la base de datos, es decir, que pone a disposición de los clientes la capacidad, no solamente de recoger los fichajes; sino también de realizar todas las gestiones necesarias relacionadas con usuarios, puntos de fichaje, dispositivos u horarios de trabajo, entre otros. Además, permite consultar los datos necesarios para la generación de informes, ya sean de absentismo laboral o de reporte de la jornada efectuada por cada operario. Esto facilita en gran medida el trabajo a llevar a cabo por las empresas, que reducen sus costes de gestión y ayuda a la toma de decisiones. ABSTRACT: In this Capstone Project, an attendance control system has been developed as a product for a company in the Information and Communication Technologies (ICT) sector in Cantabria. Its customers are mainly companies in the industrial sector, ranging from small workshops with just a few employees, to large factories with hundreds of them. Until the implementation of this software, in many of these companies, personnel check-ins and check-outs were recorded on paper or through closed systems, which did not facilitate access to data or integration with third-party systems. Under this situation, companies could not quickly and reliably know the degree of absenteeism among their workers, nor generate the necessary reports to comply with the 2019 Royal Decree on working time control; without having to sacrifice excessive time collecting data on paper and processing tedious spreadsheets.This resulted not only in lost time and financial loss associated with mismanagement, but also in constant litigation with employees over mismatches in the recording of their working time. The system presented in this project provides the necessary tools to correct these problems. On the one hand, a mobile application has been developed for Android devices, which allows employees to check-in and check-out in regardless of the environment or situation. These check ins and check-outs are registered instantly and synchronized as soon as there is an available connection on the device; which allows not only to make the check-ins and check-outs in an office; but also to collect them for those operators who work outside the facilities. On the other hand, a REST API has been developed to be executed inside an application server, and any client (such as the mobile application described above, or any other, such as a web application), can connect and interact with it through the functionality provided by it. It is responsible for storing and retrieving the information requested from the database, i.e., it provides customers with the ability not only to collect the check-ins and check-outs, but also to perform all the necessary steps related to users, control points, devices or work schedules, etc. In addition, it allows to obtain the necessary data for report generation (about absenteeism or about the amount of working time for each operator). This facilitates the work to be made by companies a lot, reduces their management costs and helps them making decisions. Grado en Ingeniería Informática

Novel Corona-virus is spreading all over the world. Millions of people have been infected with this disease and unfortunately thousands have lost their lives. Countless others have experienced the impact of Covid-19 on their health as well as in their day-to-day life. For the past one and half year as of May - 2021, the world balance and the economics have changed. Entire workflow has been shifted online, with people continuing to face a lot of difficulties due to this sudden change. But there will be a moment when normalcy will be restored, businesses bereopened and all the offices as well as other institutions start functioning as normal. This will be the most vulnerable moment as people will come in contact with each other and hence there will be a danger of this disease spreading in mass again. Hence, a system needs to be adopted to curb its spread and this can be achieved by identifying people showing the symptoms and thereby preventing them from entering organizations with a lot of attendees. The proposed system implements mask and temperature detection, sanitation while also providing a touch-less Attendance Management system for taking the attendance.

Tracking coronavirus patients and determining their close contacts (as part of COVID-19 mapping) have been huge challenges. In universities, in particular, there are many students and large gatherings who are at a higher risk of obtaining COVID-19. Many smart attendance management systems have been proposed that are based on RFID and fingerprint sensor modules, facial recognition, etc. However, these techniques operate with specific requirements, such as GPUs and large memories/datasets, or by combining recognizance and thermal cameras. To solve these issues and reduce costs, we designed an Internet of Things (IoT)-based intelligent attendance management system. In this paper, we compare the advantages/disadvantages of existing smart attendance management systems. We designed an IoT-based intelligent attendance management system based on the cloud, a web server, Google API, a non-contact body temperature sensor, and the Raspberry Pi 4 module (4G). We conducted a survey at a university and summarized the satisfaction levels of using our system.

We can make our Attendance Management System (AMS) intelligent by using a face-to-face recognition strategy. For that, we have to fix a CCTV camera in the classroom at any best point, which makes a person's picture at a fixed time and tests a face-to-face image. Traditionally, student attendance at the institutes is manually reported on the attendance sheets. It's not a productive operation, because it takes 5 or more minutes for attendance. Normally, the length of our class is 50 minutes, and every day we have more than 5 lessons. So, both courses spend more than 50 minutes, which is almost the same as our class time. For, solving this big issue we are proposed a novel automatic technique namely "Face Detection with OpenCV". The system will be connected with our master database which includes the student's name, images, roll numbers, and time of attendance. This application mainly follows three steps. Firstly, it will take images. Secondly, compare them with the existing images which are storing in the master database. Thirdly, it will mark present all the matched images automatically on a spreadsheet and the remaining students will be absent from that class.

Multimodal biometrics has engrossed a lot of interest in recent years as it offers a more dependable scheme for verification and authentication. Multimodal biometrics includes a combination of information from different biometric modalities. This research work simulates a novel method with a view of attendance management using ear and tongue images. Principal Component Analysis was employed for feature extraction of the biometric modalities and Self Organizing Feature Map was used for training and testing of the system. The method was evaluated using six thousand ear images and four thousand tongue images. The acquired images were preprocessed and features extracted were fused at this stage. The fusion results of ear and tongue images demonstrated better performance for attendance management.

Attendance sign-in is a crucial component of the daily management of enterprises and institutions, but the current approaches of attendance have problems such as sign-in deceit and low efficiency of attendance data statistics. This paper designs and develops an attendance system based on RFID (Radio Frequency Identification Devices, RFID) and face recognition. By utilizing a composite authentication method with high recognition, the reliability of attendance management and the accuracy of attendance certification are enhanced. In this system, the attendance information of employees in enterprises and institutions can be intelligently managed. Moreover, the statistical efficiency of attendance data can also be effectively promoted, thus satisfying the daily attendance needs of enterprises and institutions.

Attendance is one of the benchmarks for employee development methods and related to attendance. STMIK Palangkaraya has used the Attendance Management System (AMS) application but the report format is not as needed. This study seeks to develop a web-based data bridge application to process attendance data for lecturers and employees. The research approach used is descriptive qualitative with eXtreme Programming as a system development method. The result of this research is an application that reports the attendance of lecturers and employees in a matter of hours and minutes as well as a detailed recapitulation per person. This application can be used as a component of performance appraisal.

We all know that this is a tough time of COVID-19 with which the whole world is fighting. It is a virus that has taken many lives and affected the lack of people across the globe. It is a virus that is transmitted with close contact and droplet and is not airborne. The common symptoms include fever, cough, and fatigue. This paper focus on proposing a solution that can help detect the virus and keep people away from the infected person. The solution uses a Thermal Camera, which has a heat sensor and can detect any difference in temperature, and the camera can be integrated with access control systems in many places like Hospitals, Police stations, Factories, Universities, etc. which has staff walking in daily. The camera will not allow access to the person having high body temperature as fever is a symptom for COVID-19 and that person can be further examined for the virus. Many doctors are getting this infection while treating people, if we integrate such a solution then it can be easy to save the lives of many others up to an extent.

Conventionally, in most of the schools and colleges, attendance of students is marked manually on a sheet of paper. This results in the malpractices by students like marking fake attendance. This process is time consuming and it usually takes about ten minutes of a lecture to mark the attendance. Also, the teachers have to manually enter the attendance in spreadsheets in order to calculate the attendance percentage. This it-self is a tedious process. Although some educational institutions have started using bio-metric solutions but they are expensive and complex to use. The data collection is through the fingerprint sensor but data processing is still manual. We have used a different approach to the existing solution which not only automates the entire process but is also a cheaper alternative to the same. We have proposed a solution that sends data to google sheets which most of the teachers are familiar with. Attendance percentages are calculated and the defaulters are sent mails periodically. Additionally, the device is compact, which makes it different from others.

In recent years, facial recognition technology is not only applied for security, healthcare, business but it is also exploited creatively in education and for smart university development. In this study, an Automated Employee Attendance Management System has been designed and implemented in a university’s building for welcoming staff and supporting university administration. The system starts with a facial detection module which is based on the pre-trained MultiTask Cascaded Convolutional Network model. Then the feature vectors are created by using the ResNet34 network which results in the 128-dimension embedding vector. Face recognition is carried out by using various techniques such as K-Nearest Neighbors, Support Vector Machine. Besides the welcoming front-end module, a web-based application for querying information to manage people entering the building is also built as a back-end module. The proposed face recognition models have been trained and tested on a collected face database and a self-built face database of employees who work at the university building. The evaluation results show high recognition rates in terms of Precision, Recall, Accuracy, Fl-score and reasonable processing time. The proposed system has been piloted at the university for further development and research on face recognition technologies and smart building management.

The attendance management system is a system that needed for learning activity in a University. In some University, the attendance management system has been used tapping system which using NFC technology. Actually, that attendance management system is effective for managing each of attendance information. However, until now we can still see many cheats for this attendance system. For example, leave their cards to their classmates and tell them to tap for them. For that, one of the many effective solutions to resolve that problem is to add face recognition technology in the current attendance system. In our experiment, we know that to add face recognition to the current attendance management system surely need a camera and also face dataset for the system. At the beginning, we need at least 9 images with different emotions and face positions to let the system recognizes one's face. To make this system more accurate at recognizing one's face, we would update the face dataset in every face recognizing process.

We present a web-based system for managing student attendance by exploiting the capability of NFC tag to invoke the web browser to access the URL written in the tag. Suppose an NFC tag sticker is pasted at each seat in each classroom. Let the URL written in the tag be composed of the FQDN for the student’s web application and the query part representing the classroom and seat IDs. When a student taps the NFC tag sticker pasted at his/her seat by a smartphone, its default web browser accesses the URL written in the tag to let the student’s web application know the seat and classroom IDs. The web server puts those IDs into the session variables, prompts the student to provide the student ID with the password, gets the current time from the clock, and acquires the lecture ID from the database. Once all the session variables are filled for the session, the student’s web application registers the student’s attendance in the database. The teacher’s web application shows the student IDs on the seat map of the classroom.