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Build a complete, real-time facial recognition attendance system from scratch with this comprehensive tutorial. Learn to harness the power of Python, Flask, and OpenCV to create a web-based application that can accurately identify individuals and log their attendance.
This project is ideal for students, developers, and tech enthusiasts interested in practical applications of computer vision and machine learning. You’ll gain hands-on experience in building a system that can register new users by capturing their facial data, recognize them in a live video stream, and record their attendance with a timestamp.
User Registration: Easily add new users to the system by providing their name and ID. The application will then use the webcam to capture a set of images for training the facial recognition model.
Real-Time Face Recognition: The system uses the face_recognition library to detect and identify registered users from a live video feed.
Automated Attendance Tracking: When a registered user is identified, their attendance is automatically marked and timestamped.
Dynamic Web Interface: A user-friendly web interface built with Flask displays the list of registered users and the daily attendance log.
User Management: The system provides the functionality to view all registered users and delete them if necessary.
Python: The primary programming language for the backend logic.
Flask: A lightweight and flexible web framework for creating the application’s user interface.
OpenCV: A powerful library for real-time computer vision tasks, including accessing the webcam and processing video frames.
face_recognition: A state-of-the-art facial recognition library that makes it easy to work with facial embeddings.
HTML/CSS/JavaScript: For structuring and styling the frontend of the web application.
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