The Face Recognition Attendance System is a smart and automated solution that simplifies attendance tracking using real-time facial recognition. By utilizing a webcam, the system captures faces, identifies individuals through machine learning (K-Nearest Neighbors), and automatically logs their attendance along with a timestamp into a CSV file. This project provides an efficient and accurate alternative to traditional attendance methods, making it perfect for schools, offices, and events where manual tracking can be time-consuming and prone to errors.
Tools and Technologies:
Python: The primary programming language used for developing the system. OpenCV: A powerful library used for real-time image processing, face detection, and handling webcam input. Machine Learning (KNN): K-Nearest Neighbors algorithm is used to recognize and classify the detected faces. Pickle: A module to serialize and save the facial data and labels for future recognition. CSV: Used to store attendance logs, including names and timestamps, in an easily accessible format.