https://github.com/iamaindrik/face-attendance-system

This project implements a face recognition system to automate attendance tracking, allowing for efficient management of attendance records in educational and professional environments.
https://github.com/iamaindrik/face-attendance-system

flask knn-algorithm machine-learning python

Last synced: 19 days ago
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This project implements a face recognition system to automate attendance tracking, allowing for efficient management of attendance records in educational and professional environments.

• Host: GitHub
• URL: https://github.com/iamaindrik/face-attendance-system
• Owner: iamaindrik
• License: apache-2.0
• Created: 2024-11-03T03:51:37.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-11-06T04:23:59.000Z (6 months ago)
• Last Synced: 2025-02-14T02:24:57.693Z (2 months ago)
• Topics: flask, knn-algorithm, machine-learning, python
• Language: HTML
• Homepage: https://face-attendance-system-rho.vercel.app
• Size: 634 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Face Recognition Based Attendance System



## Table of Contents

- [Introduction](#introduction)

- [Features](#features)

- [Technologies Used](#technologies-used)

- [Algorithm Overview](#algorithm-overview)

## Introduction

The Face Recognition Based Attendance System is a robust and efficient application designed to automate the attendance process using facial recognition technology. Leveraging the K-Nearest Neighbors (KNN) algorithm, this system provides an accurate and user-friendly method to track attendance, minimizing manual input and maximizing efficiency.

## Features

- **Automated Attendance**: Automatically marks attendance based on facial recognition.

- **Real-time Processing**: Uses a live video feed to detect faces in real time.

- **User-Friendly Interface**: Simple and intuitive interface for both teachers and students.

- **Customizable**: Easy to modify and extend according to specific requirements.

- **Data Management**: Store and manage attendance records efficiently.

## Technologies Used

- Python

- OpenCV

- scikit-learn

- Flask (for web framework)

- NumPy

- Pandas

- Matplotlib (for data visualization)

## Algorithm Overview

### K-Nearest Neighbors (KNN)

KNN is a simple yet effective algorithm used for classification tasks. In this project, KNN is utilized to classify the faces of users based on their features. The algorithm works as follows:

1. **Feature Extraction**: Faces are pre-processed and features are extracted using techniques like Histogram of Oriented Gradients (HOG).

2. **Training**: The KNN algorithm is trained with labeled data (images of users).

3. **Classification**: When a new face is detected, the algorithm compares it to the existing data and identifies the closest match based on distance metrics.

Setup and Running Instructions

Step 1: Clone the Repository

Open your terminal or command prompt and run the following command:

git clone https://github.com/iamaindrik/face-attendance-system.git

Step 2: Navigate to the Project Directory

cd face-attendance-system

Step 3: Set Up a Virtual Environment (Optional but Recommended)

Install virtualenv if you haven't:

pip install virtualenv

Create a virtual environment:

virtualenv venv

Activate the virtual environment:



    
On Windows: 
venv\Scripts\activate



    
On macOS/Linux: 
source venv/bin/activate





Step 4: Install Dependencies

Run the following command to install all required packages:

pip install -r requirements.txt

Step 5: Run the Application

Use the following command to start the Flask application:

python app.py

Step 6: Access the Application

Open your web browser and go to the URL displayed in the terminal, typically:

http://127.0.0.1:5000