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👤 Face Recognition System

A comprehensive face recognition system built with Python, OpenCV, and Tkinter that allows for face detection, training, and recognition.

🌟 Features

• 🔍 Face Detection and Recognition
• 📚 Training Data Management
• 🖥️ User-friendly GUI Interface
• ⚡ Real-time Face Recognition
• 💾 Database Integration (MySQL)
• 👥 Multiple User Support
• 📊 Attendance Tracking
• 🔒 Secure Authentication

🛠️ Prerequisites

Before running this project, make sure you have the following installed:

• 🐍 Python 3.x
• 📸 OpenCV (cv2)
• 🖼️ PIL (Python Imaging Library)
• 🗄️ MySQL Connector
• 🎯 Tkinter (usually comes with Python)

📥 Installation

1. Clone the repository:

git clone https://github.com/rohitbansal2005/Face-Recognition-Attendance-System-Software

2. Install the required packages:

pip install opencv-python
pip install pillow
pip install mysql-connector-python
pip install numpy

3. Set up your MySQL database and update the connection details in the code.

📁 Project Structure

Face Recognition System/
├── Data/                  # Directory for storing face images
├── college_images/        # Directory for UI images
├── train.py              # Training module
├── face_recognition.py   # Main recognition module
├── face_detector.py      # Face detection module
└── README.md

🔄 System Flow Diagram

graph TD
    A[Start] --> B[Initialize System]
    B --> C{User Selection}
    C -->|Training| D[Load Training Data]
    C -->|Recognition| E[Start Camera]
    D --> F[Process Images]
    F --> G[Train Classifier]
    G --> H[Save Model]
    E --> I[Face Detection]
    I --> J[Face Recognition]
    J --> K[Database Update]
    K --> L[Display Results]
    H --> M[End]
    L --> M

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🚀 Usage Guide Step 1: Start the Application 1.Run the main application:

python login.py

2.The system will open the Login Interface.

Step 2: Register Users After logging in, navigate to the Registration Interface: -The registration module allows you to add new users to the system.

Step 3: Capture Photos for Face Recognition 1.Open the Student Management System Interface. 2.Use the Photo Sample option to capture images of the user's face: -These photos will be stored in the Data directory for training.

Step 4: Train the System 1.Run the training module:

python train.py

2.Click on the "TRAIN DATA" button to process all the captured images and train the classifier.

Step 5: Perform Face Recognition 1.Once training is complete, proceed to the Face Recognition Interface: -This interface allows real-time face recognition using the trained classifier.

2. Use the interface to:
   • 📸 Capture new face images
   • 🎓 Train the system
   • 🔍 Perform face recognition
   • 📊 View attendance records

💾 Database Setup

1. Create a MySQL database
2. Update the database connection details in the code
3. The system will automatically create necessary tables

Database Schema

CREATE TABLE student (
    student_id INT PRIMARY KEY,
    name VARCHAR(100),
    roll_no VARCHAR(20),
    department VARCHAR(50),
    face_id INT
);

CREATE TABLE attendance (
    attendance_id INT PRIMARY KEY AUTO_INCREMENT,
    student_id INT,
    date DATE,
    time TIME,
    status VARCHAR(20),
    FOREIGN KEY (student_id) REFERENCES student(student_id)
);

🔧 Technical Details

Face Detection Process

1. Image Capture
2. Grayscale Conversion
3. Face Detection using Haar Cascade
4. Face Region Extraction

Recognition Algorithm

• LBPH (Local Binary Pattern Histogram) Face Recognition
• Feature Extraction
• Pattern Matching
• Confidence Score Calculation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

🙏 Acknowledgments

• OpenCV for face detection and recognition capabilities
• MySQL for database management
• Tkinter for GUI development

❓ Support

For support, please open an issue in the repository or contact the maintainers.

📞 Contact

• 📧 Email: [email protected]

📝 Version History

• v1.0.0 - Initial Release
• v1.1.0 - Added Attendance Tracking
• v1.2.0 - Enhanced UI/UX
• v1.3.0 - Added Real-time Recognition

🔒 Security Considerations

• Secure storage of face data
• Encrypted database connections
• Access control implementation
• Regular security updates

🌐 System Requirements

• Windows 10/11 or Linux
• 4GB RAM minimum
• 2GB free disk space
• Webcam with 720p resolution minimum