CLASS VISION - Smart Attendance System

A modern, AI-powered attendance management system that uses facial recognition technology to automate student attendance tracking with high accuracy and efficiency.

✨ Features

• 🤖 AI-Powered Face Recognition - Advanced facial detection and identification
• 📸 Smart Registration - Easy student enrollment with automated image capture
• ⚡ Real-time Attendance - Instant attendance marking with live camera feed
• 📊 Automated Reporting - Generate detailed attendance reports in CSV format
• 🎨 Modern Dark UI - Eye-friendly interface with responsive design
• 🔒 Secure & Reliable - Local data storage with no cloud dependencies

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• Webcam
• Windows/Linux/macOS

Installation

1. Clone the repository

   git clone https://github.com/HARSHGUPTA10123/CLASS-VISION.git
   cd CLASS-VISION

2. Install dependencies

   pip install -r requirements.txt

3. Create Required Folders

   # Create necessary directories for the application
   mkdir TrainingImage
   mkdir TrainingImageLabel
   mkdir StudentDetails
   mkdir Attendance

4. Run the application

   python attendance.py

📋 How It Works

1. Student Registration

• Click "Register" button
• Enter Enrollment Number and Name
• System captures 30 facial images automatically and stores in TrainingImage/ folder
• AI model trains on the captured data

2. Take Attendance

• Select "Take Attendance"
• Choose subject name
• System detects faces in real-time and marks attendance
• Records saved with timestamps in Attendance/ folder

3. View Reports

• Click "View Attendance"
• Browse attendance records by subject from Attendance/ folder
• Export data for further analysis

🗂️ Project Structure

CLASS-VISION/
├── attendance.py              # Main application file
├── automaticAttedance.py      # Automatic attendance module
├── takeImage.py              # Image capture module
├── trainImage.py             # Model training module
├── show_attendance.py        # Attendance viewing module
├── haarcascade_frontalface_default.xml  # Face detection model
├── requirements.txt          # Python dependencies
├── TrainingImage/            # Student face database (create this folder)
├── TrainingImageLabel/       # Trained model files (create this folder)
├── StudentDetails/           # Student information (create this folder)
├── Attendance/               # Attendance records (create this folder)
└── UI_Image/                 # Application icons and images

🛠️ Technical Details

Technologies Used

• Python - Core programming language
• OpenCV - Computer vision and image processing
• Tkinter - GUI development
• PIL - Image handling
• NumPy - Numerical computations
• Pandas - Data management
• Haar Cascades - Face detection algorithm
• LBPH - Face recognition algorithm

Face Recognition Pipeline

1. Face Detection - Haar Cascade classifier
2. Image Preprocessing - Grayscale conversion and normalization
3. Feature Extraction - Local Binary Patterns Histograms (LBPH)
4. Model Training - Supervised learning on captured images stored in TrainingImage/
5. Real-time Recognition - Live camera feed processing

📸 Screenshots

FOR SCREENSHOTS VISIT THIS LINK:-

https://drive.google.com/drive/folders/1Ra9DFry6_WJ33CBOjV0W4rTjpRR3QoeM?usp=sharing

Update these paths in the respective files if needed:
```python
# File paths configuration
haarcasecade_path = "haarcascade_frontalface_default.xml"
trainimagelabel_path = "./TrainingImageLabel/Trainner.yml"
trainimage_path = "./TrainingImage"
studentdetail_path = "./StudentDetails/studentdetails.csv"

🐛 Troubleshooting

Common Issues:

1. "TrainingImage folder not found"

   # Solution: Create the folder manually
   mkdir TrainingImage

2. Camera access denied

   • Ensure no other application is using the camera
   • Grant camera permissions to Python

3. Dependencies installation failed

   pip install --upgrade pip
   pip install -r requirements.txt

👨‍💻 Developer

Harsh Gupta

• GitHub: @HARSHGUPTA10123
• Project: CLASS VISION Attendance System

🙏 Acknowledgments

• OpenCV community for excellent computer vision libraries
• Python community for comprehensive documentation