Face recognition-based attendance system is a process of recognizing the faces of the students while taking attendance by using face bio-metrics based on high – definition monitor video and other information technology. In this face recognition project, a computer system will be able to find and recognize human faces quickly and precisely in images or videos that are being captured through a webcam / a surveillance camera.

Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a PDF report of the attendance of the student.

The project that we worked on this summer internship falls in the domain of research in IoT (Internet of Things). Initially, the mentor asked us to find real-life problems, which we would attempt to solve by using the tools of Information Technology. We were allowed to discuss and work in a group of three. We picked the problem of devising an attendance monitoring system, which would mark the presence of the students in a big room, in a non-intrusive manner using image recognition, for e.g. an auditorium or our college’s lecture theatre. Our project was divided into two phases, which would be illustrated in the subsequent passages. The first phase involved doing a literature survey on the tools and technologies through various authentic research papers and the existing libraries, which would enable us to devise a backend structure for our project. We, then developed a flowchart, which comprised of two modules of processes, through which the procedure would pass through. The first module involves the initial training of a machine learning based classifier by training it with the various images of a specific person. The second module involves the testing part in the real environment, which involves face detection and face recognition. A camera would take the frames/image of a live audience. Then, these frames would be pre-processed (involves grey-scaling and image resizing) for achieving better performance in the subsequent face detection module. The face-detection algorithm would detect all the faces present in the frame, and would crop the detected faces, and would pass them to the face recognition classifier for testing. The classifier would classify the cropped images and would mark the attendance accordingly. The libraries used for face-detection were that of OpenCV, and a convolutional neural network was trained for the image recognition part. The libraries which were used for training the convolutional neural network was Keras. The second phase involved the implementation part, where we had to gather the data for training the neural network, and find out the parameters of the image, for which we are getting better accuracy performance. We trained the neural network with the images of about 64 students, with about 20 images per student, covering different angles and brightness levels. We trained the network with 70 percent of the image corpus, and used the remaining 30 percent for testing. We got an accuracy of 93 percent. For testing the face detection part, we took a video of a classroom of about 40 students. Then, we generated frames from the video and passed it to the face detection algorithm. We extrapolated that the accuracy of an individual frame was not that high, but if we consider all the detected members in all the frames, we are covering almost every student. Hence, considering multiple frames for testing is crucial to get a high detection accuracy. We are currently trying to figure out the camera and its mounting position, which would be conducive for the algorithm, to give us accurate results.

Facial recognition could soon jump from your smartphone to your workplace with employers using it to mark attendance and gauge the mood of the workforce.Every day, corporate offices and institutes are working to increase the productive working hours in a day. When the current system of clocking in daily using a fingerprint scanner is a time-consuming and inefficient use of time. I have planned to design a Voice Interactive Face Detection Based Smart Attendance management and behavior analysis to ensure a better work culture and environment,efficiency in a secure manner using Intel dev cloud. Currently, we have fingerprint and Smart-card Based entries in nearly all offices and a few schools and colleges. These system then automates the calculation of salary or attendance percentage.But fingerprint scanning and smart card barcode entries tend to take up time and prove to also be imperfect. In contrast, Face Recognition method provides a unique feature for every individual which is stored in a central database and can be retrieved during recognition and validation. The system includes an embedded application deployed in a SCB( Single Board Computer) which can interact with the users in real time. It will take down in and out time of every employee and monitor their working behavior(future scope) and notify the corresponding employee and the authority at times. We are aiming to analyze people's behavior,mood and emotions by monitoring and studying their actions in real time which in turn will help the organization know about the physical and mental status of the employees. This process of direct integration of physical world into computer vision based systems will indeed result in efficiency improvements, economic benefits and reduced human exertions. As of now I have developed a basic voice interactive attendance monitoring using Jupyter Notebook on Intel dev cloud. The in and out time (including mid in and out) will be monitored in Google spreadsheet and the system will calculate how many hours an employee has spent in office premises. The system won’t allow employees to step into the office after a certain time and won’t consider the attendance if the total hours spent is less than four hours. Everyday a mail will be sent to the admin containing the attendance details of the employees. In future, I would like to implement behavior and mood analysis of the employees and the staff on the office premises which in turn will help the concerned staff provide with solutions to get over the listless mood or erratic behavior.

Authentication is a significant issue in system control in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This project describes a method for Student’s Attendance System which will integrate with the face recognition technology using deep learning algorithms. The system will recognize the students present in the classroom and provide the list of present students for the lecture. The primary technique used for the face detection is by using python inbuilt packages of OpenCV. Once the model is trained on different kinds of datasets, the project will help in identifying students present for the class. The front end will be based on an android application. The application uses SQLite database for establishing connection between web app and the model. The backend model mainly comprises of a convolutional neural network which extracts features and trains the model in recognizing those features. The inbuilt OpenCV uses haarcascade classifiers in identifying the faces present in the input image. The list of identified will be displayed as the end result.

Developed a system to track down the attendance of students using the face recognition technique and also can monitor them

Attendance Monitoring System that has a tracker, face detection, face recognition and database connectivity all integrated together. The tracker is based on Strongsort, yolov8 is used for face detection, InceptionResnet is used for face recognition and MySQL for database connectivity.

An attendance monitoring system using face recognition and liveliness detector.

A smart, AI-powered attendance monitoring system that uses QR code verification and face recognition for fast, secure, and automated attendance tracking. This repository includes the complete source code along with a detailed project report covering system architecture, methodology, implementation, and results.

A python GUI integrated attendance system using face recognition to take attendance.

"Automatic Attendance Monitoring System" To maintain the attendance record in schools colleges or any administrative sector through a conventional method by calling the name of every person is very difficult and time-consuming and even a chance of proxy attendance so the following system is based on face recognition to maintain the attendance record of the students and workers. In our implementation, we have tried to recognize faces and marking down the attendance using Haar Cascades with the 'Local Binary Pattern Histogram' technique. The graphic user interface is designed using the Tkinter library. This monitoring system gives the appropriate tracking of the students attending the schools/ colleges with real-time data stored in a separate file. On the contrary, this is an offline system which is the only limitation for this system. Picture Description: 1. Tkinter GUI of the system - 2. Face recognized using this system - 3. Record stored in real-time

Attendance monitoring system using face recognition developed as part of internship at Tequed Labs

The Facial Recognition Attendance System (FRAS)is designed to automate the attendance tracking process using facial recognition technology. This system utilizes a webcam to capture live video feed, detects faces, and recognizes registered faces while ensuring liveness detection by monitoring eye blinks.

The Employee and Work Monitoring System is a comprehensive solution designed to track employee attendance and performance using face recognition technology. This system can be effectively utilized in offices, schools, and colleges to monitor employee attendance, work hours, and productivity.

The main purpose of this project is to build a face recognition-based attendance monitoring system for educational institution to enhance and upgrade the current attendance system into more efficient and effective as compared to before. The manual entering of attendance in logbooks becomes difficult and takes a lot of time also, so we have designed an efficient module that comprises of face recognition using LBPH algorithm(OpenCV) to manage the attendance records of employee or students. During enrolling of a user, we take multiple images of a user along with his/her id/roll number and name. The presence of each student/employee will be updated in Excel Sheet. This process can give us more accurate results in user interactive manner rather than the existing attendance systems. This also gives students/employees a more accurate result in user interactive manner rather than existing attendance management system.

Smart Face Recognition Attendance System – Real-time employee attendance tracking using YOLOv8 and face_recognition. Automatically detects known faces, logs entry/exit times, and handles unknown faces with optional saving. High-speed detection, dynamic database updates, and full-screen/resizable interface for easy monitoring.

Attendance system using Face Recognition: This is project built using Python, HTML, CSS, Javascript tech stack. It involves different stages of attendance system like Registration, Delete attendance, monitoring of registration, log the details, query old records, etc.

The purpose of the attendance monitoring system using face recognition is to ease the attendance process which consumes lot of time and efforts; it is a convenient and easy way for students and teacher. The system will capture the images of the students and using face recognition algorithm mark the attendance in the sheet.

SAM (Smart Attendance Monitoring) is an innovative attendance portal that uses advanced face recognition technology to automate the attendance management process. By leveraging artificial intelligence and computer vision, SAM reduces manual errors and saves time for educational institutions and organizations.

Key Features - Real-time face recognition through live camera - Works with existing CCTV or webcam setups - Automatic attendance tracking (IN / OUT capture) - Multi-person detection and recognition - Can be used for employee monitoring and theft prevention - Attendance data logging for analysis and reporting - Contactless and automated system

This repository features an AI-powered attendance system that uses Faster R-CNN for face recognition, achieving 98.87% accuracy. The system captures real-time student images to automatically mark attendance and monitor classroom activity.

AI-powered classroom attendance and engagement monitoring system. This web application uses face and voice recognition for reliable, no-proxy attendance. It provides real-time attention heatmaps and an AI-based Learning Retention Monitor to give teachers instant feedback on student focus and potential confusing concepts.

Face recognition is an important application it is used in numerous fields. This application is use for maintain and monitoring the attendance records. It is play important role in any organization. Marking and managing attendance is a difficult task by educational institutes, which is timing-consuming and leads to many student and teacher’s related issues. To solve these issues there should be an automatic system of managing and marking attendance. The purpose of this application Faces Recognition Base Attendance Management System. We made the system capable of identifying the faces of the students and base on the matched faced will give in the database and marked attendance automatically. The automatic system will maintain the record of attendance for future use it will also calculate the short attendance of the students.

The main purpose of this project is to build a face recognition-based attendance monitoring system for educational institution to enhance and upgrade the current attendance system into more efficient and effective as compared to before. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Many problems arise when the authority is unable to enforce the regulation that exist in the old system. The technology working behind will be the face recognition system. The human face is one of the natural traits that can uniquely identify an individual. Therefore, it is used to trace identity as the possibilities for a face to deviate or being duplicated is low. In this project, face databases will be created to pump data into the recognizer algorithm. Then, during the attendance taking session, faces will be compared against the database to seek for identity. When an individual is identified, its attendance will be taken down automatically saving necessary information into a excel sheet. At the end of the day, the excel sheet containing attendance information regarding all individuals are mailed to the respective faculty.

Recognition of the human face is an active issue for authentication purposes specifically in the context of attendance of students. Attendance system using face recognition is a procedure of recognizing students by using face biostatistics based on the high definition monitoring and other computer technologies

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Attendance Monitoring System Using Face Detection and Face Recognition

This is a Smart India Hackathon-2018 project .This project aims in continuous monitoring of staff's using face recogntition with python,opencv and pi

Face Recognition and Attendance Monitoring using Artificial Intelligence