Prakhar Agarwal Logo Image
Prakhar Agarwal

Multi-Face Recognition Attendance System

Built an automated attendance system using deep CNN and k-NN, achieving 99.24% accuracy on the LFW dataset.

Project Image

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.

Tools Used

OpenCV
Pandas
dLib
NumPy
Python