Face-Recognition-Attendance-System

The Face Recognition Attendance System is an intelligent and automated attendance management solution that aims to simplify and improve the traditional attendance process. Many educational institutions and organizations still record attendance manually. This method takes a lot of time, requires significant human effort, and is prone to errors like proxy attendance and inaccurate record keeping. To address these issues, this project uses facial recognition technology, computer vision, and machine learning techniques to automatically identify individuals and record their attendance in real time. The system is developed with Python as the main programming language and incorporates technologies such as Flask, OpenCV, NumPy, and Scikit-Learn. A webcam captures live video streams, and facial images are detected using the Haar Cascade Classifier. The extracted facial features are processed through a pre-trained K-Nearest Neighbors (KNN) classifier to accurately recognize registered users. When a face is successfully identified, the system automatically records the user's attendance along with the date and time in a database. This system provides a secure, efficient, and user-friendly alternative to traditional attendance methods. It reduces manual effort, lowers the chance of fraudulent attendance practices, and improves overall accuracy and reliability. The web-based interface enables administrators to register new users, train the recognition model, monitor attendance records, and manage user information effectively. This project shows how Artificial Intelligence and Computer Vision can solve real-world administrative problems. The system can be adopted widely in schools, colleges, universities, offices, and other organizations that require attendance monitoring. Future improvements may include deep learning-based face recognition, cloud database integration, mobile application support, and advanced reporting features to enhance performance, scalability, and security.