Face Detection and Recognition System

A computer vision project demonstrating face detection and recognition capabilities using OpenCV and the face_recognition library. This system allows users to upload images, detect faces, and match them against a database of known faces.

Features

• Face Detection: Identifies and locates faces in images using OpenCV
• Face Recognition: Matches detected faces against known faces using face_recognition library
• User-friendly Interface: Simple web interface built with Streamlit
• Database Management: Easily add new people to the recognition database

Installation

1. Clone this repository:

git clone https://github.com/Assem-ElQersh/Face-Recognition-System.git
cd Face-Recognition-System

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install system dependencies (required for face_recognition):

   For Ubuntu/Debian:

   sudo apt-get install cmake
   sudo apt-get install libboost-all-dev

   For Windows: See detailed instructions on the dlib installation guide

5. Create sample data (test images):

python create_sample_data.py

Usage

1. Start the web interface:

streamlit run ui/app.py

2. Navigate to the provided URL in your browser (typically http://localhost:8501)

3. Upload an image to detect faces

4. Register new faces by adding labeled images to the data/known_faces directory (one subdirectory per person)

Sample Data

The system includes a script to create sample data automatically. Run python create_sample_data.py to:

1. Create the necessary directory structure
2. Download sample face images from the OpenCV repository (if available)
3. Generate synthetic face images if downloads fail

The script will populate:

• data/known_faces/person1/ and data/known_faces/person2/ with individual face images
• data/sample_images/ with group images for testing detection and recognition

If you want to use your own images:

• Place individual face images in the appropriate person folders (e.g., data/known_faces/john/image1.jpg)
• Place group/test images in the data/sample_images/ directory

Repository Structure

Face-Recognition-System/
├── README.md                     # Project documentation
├── QUICKSTART.md                 # Quick start guide
├── requirements.txt              # Dependencies
├── main.py                       # Main application entry point
├── create_sample_data.py         # Script to create sample data
├── face_detector.py              # Face detection module
├── face_recognizer.py            # Face recognition module
├── utils.py                      # Utility functions
├── ui/
│   ├── __init__.py
│   ├── app.py                    # UI application using Streamlit
│   └── components.py             # UI components
├── data/
│   ├── known_faces/              # Directory for known face images
│   │   ├── person1/              # Each person gets their own directory
│   │   │   ├── image1.jpg
│   │   │   └── image2.jpg
│   │   └── person2/
│   │       ├── image1.jpg
│   │       └── image2.jpg
│   └── sample_images/            # Sample images for testing
│       ├── group1.jpg
│       └── group2.jpg
└── models/                       # Directory for model files (if needed)

Command Line Interface

In addition to the web interface, you can use the command-line interface:

Face Detection:

python main.py --mode detect --image path/to/image.jpg --output results/output.jpg

Face Recognition:

python main.py --mode recognize --image path/to/image.jpg --output results/output.jpg

Add a Face:

python main.py --mode add_face --image path/to/face.jpg --name "Person Name"

Encode Known Faces:

python main.py --mode encode

Requirements

• Python 3.7+
• OpenCV
• face_recognition
• dlib
• numpy
• Streamlit

Future Improvements

• Add real-time face recognition using webcam
• Implement user authentication system
• Add database integration for storing face encodings
• Improve recognition accuracy with additional algorithms

License

This project is licensed under the MIT License - see the LICENSE file for details.