This project implements a face recognition system using Python, OpenCV, and SQLite. The system captures video from a webcam, detects faces, recognizes them using a trained model, and retrieves user information from a SQLite database.

• Features
• Requirements
• Installation
• Usage
• File Structure
• License

• Real-time face detection and recognition.
• User information retrieval from a SQLite database.
• Easy to set up and run.

• Python 3.x
• OpenCV
• NumPy
• SQLite

You can install the required libraries using pip:

pip install opencv-python numpy

1. Clone this repository:

   git clone https://github.com/mimi-netizen/Face-recognition.git
   
   cd Face-recognition

2. Download the Haar Cascade file for face detection:

   • Ensure haarcascade_frontalface_default.xml is present in the project directory.

3. Set up the SQLite database:

   • Create a SQLite database (sqlite.db) and a table named STUDENTS with appropriate fields.

1. Create Dataset:

   • Run dataset_creator.py to capture images for training.

   python dataset_creator.py

2. Train the model (if not already trained):

   • Run the trainer.py script to train the face recognizer using images stored in the dataset.

   python trainer.py

3. Start the face recognition application:

   python detect.py

4. Press q to exit the application.

Face-recognition/
│
├── .idea/                     # IDE-specific files
├── __pycache__/               # Compiled Python files
├── haarcascade_frontalface_default.xml  # Haar Cascade file for face detection
├── database.db                # SQLite database file
├── dataset_creator.py         # Module to create datasets
├── detect.py                  # Main detection script
├── sqlite.db                  # SQLite database file
├── trainer.py                 # Script to train the face recognizer
└── README.md                  # Project documentation

• Customize the database setup instructions according to your actual database schema.
• Ensure that all necessary files are correctly referenced and included in your repository.
• You may want to include more detailed instructions or features based on your specific implementation.