Project Overview
This project is an automated attendance system that leverages facial recognition technology for schools, colleges, and workplaces. Using dlib’s face recognition algorithm, it achieves 99.24% accuracy on the LFW dataset. Deep convolutional neural networks (CNN) are used for face encoding, and k-Nearest Neighbors (k-NN) is applied for classification. The system eliminates the need for manual attendance tracking, making the process more efficient and accurate.
Key Features: Employs deep CNN for face encoding and k-NN for classification. Achieves a high recognition accuracy of 99.24% on the LFW dataset. Automates attendance logging, reducing errors and manual effort. Can be integrated with existing school and workplace management systems.