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.

Attendance Management System based on Face Recognition using Python and OpenCv

This is the Python FYP Face Recognition Attendance System from the machine learning base By Teach Learn School.

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.

A demo python application for face recognition based attendance system using enhancement of low resolution images by super resolution deep learning models.

Python implementation of simple face recognition based attendance system using face_recognition library.

Face recognition based Attendance Management System by using OpenCV and python with a Tkinter GUI interface.

Face Recognition based attendance system for classroom environment. Developed a python API which recognizes the people in a picture(of a classroom) and matches them with all the student registrations for that course and returns a image with all the recognized and unrecognized faces (face tags), and tags all the students recognized as present.

This GitHub repository contains a web-based Facial Recognition Attendance System built with Python language and Streamlit framework. The System built with Face Recognition using Inception Resnet (V1) models in pytorch, pretrained on VGGFace2 and CASIA-Webface datasets, also Anti-Spoofing models by Minivision.

1.Introduction: The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric face recognition technology has received significant attention in the past several years due to its potential for a wide variety of applications in both law enforcement and non-law enforcement. As compared with other biometrics systems using fingerprint/palmprint and iris, face recognition has distinct advantages because of its non-contact process. Face images can be captured from a distance without touching the person being identified, and the identification does not require interacting with the person. In addition, face recognition serves the crime deterrent purpose because face images that have been recorded and archived can later help identify a person. 2.Recognition Process: Face Recognition Process is classified into three Phases as DataCreation Phase,Training Recognizer Phase,Detection Phase. 2.1 DataSet Creation Phase In this phase, pictures are captured and stored it in Dataset Folder. 2.2 Training Recognizer Phase In this Phase,the dataset is feed to LBPH Recogniser and trained the classifier. 2.3 Detection Phase In this Phase,the classifier is feed with new image and predicts the image either with known or unknown values. 3. Updating Attendance Finally the Image names and their attendance is marked in Excel sheet by camparing the current Id and Id in the sheet.

🎯 Python-based face recognition attendance system using OpenCV and face_recognition.

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.

This repository contains code for facial recognition using openCV and python with a tkinter gui interface. If you want to test the code then run train.py file

This is a face recognition-based attendance system developed in Python. The system utilizes the OpenCV library along with other modules to recognize faces and mark attendance in a CSV file.

Attendify is a web-based AI attendance system that uses facial recognition to automate tracking. Teachers upload classroom photos, and the system detects and matches student faces with the database to mark attendance. Built with Python, OpenCV, React.js, and MySQL/MongoDB, it saves time, reduces errors, and improves efficiency.

Python-based Face Recognition Attendance System with real-time logging, user registration, and a simple GUI.

Face Recognition based attendance system build using python libraries involves the concept of convolutional neural network.

The Student Attendance System is a Python-based application designed to simplify the process of student attendance using face recognition technology. This project combines the power of machine learning with an interactive PyQt GUI to offer a user-friendly and efficient solution for managing attendance records.

The attendance check is hugely crucial in the management of the classroom. The time-consuming way of taking attendance is by the following prior methods such as signing the name in a paper sheet or calling the names of each student. In particular, it is vulnerable to cheating and other fraudulent activity. Artificial Intelligence based applications benefit from face recognition, which is a valuable piece of work. The suggested system aims to develop a comprehensive face recognition system capable of dealing with many photos. The central technological concept of this system is Machine Learning to detect human faces and identify a person. Haar-Cascade is a face detection algorithm that identifies the face from a sequence of photos used for training and testing. The dataset uses negative and positive images to train the algorithm called Local Binary Pattern Histogram (LBPH) and the same data has been used to check the system. The LBPH algorithm is responsible for recognizing faces in input images. Python Tkinter is very effective in making desktop GUI software, So the core element of the developed system is Tkinter. The database of the system is MySQL. With the capability of this technology trialed in a class, the outcome result is highly positive — a survey completed among students to inspect the students’ opinion and find out the advantages and disadvantages.

Attendace System with Face Recognition in Python

A web based system for managing student attendance using face recognition and geolocation built with Python 3.6 and JS

# Face_recognition_based_attendance_system A python GUI integrated attendance system using face recognition to take attendance. 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. TECHNOLOGY USED: 1) tkinter for whole GUI 2) OpenCV for taking images and face recognition (cv2.face.LBPHFaceRecognizer_create()) 3) CSV, Numpy, Pandas, datetime etc. for other purposes. FEATURES: 1) Easy to use with interactive GUI support. 2) Password protection for new person registration. 3) Creates/Updates CSV file for deatils of students on registration. 4) Creates a new CSV file everyday for attendance and marks attendance with proper date and time. 5) Displays live attendance updates for the day on the main screen in tabular format with Id, name, date and time.

The Attendance system is based on the machine learning algorithm which is to be implemented on python language and using computer/laptop camera for the input image of the students or a normal outer camera can also be used which has to be connected to the system which is programmed to handle the face recognition by implementing the CNN algorithm.

AI-based attendance system using face recognition (Python)

This is a Python based Face Recognition Attendance System.

Face recognition based Attendance Management System by using OpenCV and python, where c# is used for visualization.

This project is a real-time face recognition-based class attendance system built using Python, OpenCV, and the face_recognition library. It leverages computer vision to detect and recognize student faces through a webcam and logs their attendance into a CSV file.

This project deals with one of the most efficient and accurate Attendance systems based on facial Recognition. Face recognition provides an accurate method that solves ambiguities such as fraudulent attendance and time consumption since it is understood that any human's primary identity is their face. This project uses OpenCv library with python to register, train, and recognize face of a particular individual through a front-end on Node-RED dashboard and then store the data of the individual whose is not recognized into IBM Cloud Object and Cloudant database and notify the admin with a SMS consisting of the link where the image taken of that individual is stored. It also uses the excel sheet to mark the people present on the particular day.

Face Recognition based Attendance System using Opencv Python and pillow

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.