Final Year Btech Face recognition Attendance System Project with code and Documents. Video Implementation with explanation too. Base IEEE paper Implementation

This project is a face recognition-based attendance system that uses OpenCV and Python. The system uses a camera to capture images of individuals and then compares them with the images in the database to mark attendance.

Software Engineering course project, Smart Attendance System using Face Recognition and OpenCV

This project is a comprehensive face recognition-based attendance system for universities. It leverages OpenCV for face detection and recognition, Firebase for data storage, and Flask for the web interface. The system allows for student registration, face capture, and attendance tracking, providing a modern solution for attendance management.

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.

The "Face recognition attendance system" is a hardware prototype of a face recognition attendance system. This project is developed using Rasberry pi, RPI camera , OpenCV and Python coding.

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.

This Project was made for the purpose of taking attendance by face recognition, I used several Python3 libraries to obtain a system to track attendance by face recognition.

This project involves automating the attendance system of RT Knits using Face Recognition. Due to Covid-19, people are obliged to wear masks hence, the system is successful in recognizing people despite wearing masks.

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.

Face Recognition Attendance System is a project build for the sole purpose of optimizing the attendance system of schools/colleges

This project is a web application demonstrating the use of facial recognition for marking attendance. It can be used by the company to manage attendance of its employees and generate attendance report/stats. It Reduces manual process errors by providing automated and a reliable attendance system that uses face recognition technology.

A complete GUI based attendance system project for real time face recognition attendance using existing CCTV cameras in

# Attendance-System-Face-Recognition This project is a web application demonstrating the use of facial recognition for marking attendance built as a part of my PS -1. It is a web application that can be used by the company to manage attendance of its employees. ## Functionality Supported - Admin and Employee Login - Admin : Register new employees. - Admin : Add employee photos to the training dataset. - Admin: Train the model. - Admin: View attendance reports of all employees. Attendance can be filtered by date or employee. - Employee - View attendance reports of self. ## Built Using - **OpenCV** - Open Source Computer Vision and Machine Learning software library - **Dlib** - C++ Library containing Machine Learning Algorithms - **face_recognition** by Adam Geitgey - **Django**- Python framework for web development. ### Face Detection - Dlib's HOG facial detector. ### Facial Landmark Detection - Dlib's 68 point shape predictor ### Extraction of Facial Embeddings - face_recognition by Adam Geitgey ### Classification of Unknown Embedding - using a Linear SVM (scikit-learn) The application was tested on data from 9 employees.

This a very impressive Face-recognition project. by following this we can implement a security system as well as a attendance system for college/schools and offices.

This code is used for my college project of Attendance system using Face-Recognition and OpenCV library of Python 3.9.5. The code skeleton and outline is used from this video: https://youtu.be/uwJltCOrpEI and edited the code for necessory changes to make it run properly.

Advance Face Recognition Student Attendance System Project in Python OpenCV With Tkinter GUI & Mysql Database

Advance Face Recognition Student Attendance System Project in Python OpenCV With Tkinter GUI & SQLite Database. Features of Project: real time face detection 1] Home Page i) Student management system (Save, Take Photo Samples, Update, Delete, Clear) ii) Train Photo Samples iii) Take Attendance with Face Detection iv) Attendance Report (Excel file & SQLite database) v) Developer Page vi) Help Desk.

In this python project, I have made an attendance system which takes attendance by using face recognition technique. I have also intergrated it with GUI (Graphical user interface) so it can be easy to use by anyone. GUI for this project is also made on python using tkinter.

The main objective of the proposed project is to build a face recognition attendance system for monitoring the attendance of the students coming in contact with the camera. Many problems arise when the authority is enable authorities regulation of the old attendance system. The technology working behind will be the face recognition system. It recognises the human face and records the attendance. It can be viewed on the users smartphone and get the monthly attendance file.

Python-OpenCV-Django Face Recognition Attendance System Project For Marking Attendance Automatically On Facial Recognition Generally Used In College And Offices.

This project implements a Face Recognition Attendance System utilizing the VGGFace model for face embedding extraction and recognition.

This project is an AI-powered attendance system that utilizes face recognition and emotion detection to mark attendance automatically.

Student attendance management system for the browser using face recognition on video input from user's webcam with face-api.js by @justadudewhohacks. This is a MERN stack web development project building on previous work.

It performs Facial recognition with high accuracy. This attendance project uses webcam to detect faces and records the attendance live in an excel sheet. In order to determine the distinctive aspects of the faces based on distance, convolutional neural networks are used. All you need to do is stand in front of the camera and your face is verified instantly in milliseconds, without recording the attendance more than once. Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

S.Y. B.Tech Mini Project: Automatic Attendance System based on Face Recognition.

using this project one can take attendance with face recognition system. A full attendance system with options of taking attendance manually or by face recognition has been built with the help of emgu cv 3.0 library .

The project aims to develop a biometric face recognition-based attendance system, incorporating thermal scanning and CCTV as additional features. These features will be managed by a web-based data management system.

The project is about Face recognition based Attendance System. By using this system, attendance can be taken simply facing the camera. This system has the ability to detect any face and based on training data it can recognize and register the attendance for that student. It is very simple and easy way to take attendance in any class using this system.

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