Real Time Face Recognition (OpenCV)

Create a fast real-time face recognition app with Python and OpenCV.

Installation

pip install -r requirements.txt

Required packages:

• opencv-python
• opencv-contrib-python
• pillow
• pyyaml

Configuration

All settings are stored in src/config.yaml:

• Camera settings (resolution, device index)
• Face detection parameters
• Training parameters
• File paths
• Confidence threshold (how confident the model has to be to recognize a face)

You can modify these settings without changing the code.

Usage

The system works in three steps:

1. Capture Face Data

Run face_taker.py to capture training images:

python src/face_taker.py

• Enter your name when prompted
• 🚨 The script captures 120 images of your face. Make sure to have a good lighting and move your head around to capture different angles.
• Keep your face centered in the frame
• Images are saved in the images folder
• Your name and ID are stored in names.json
• Press 'ESC' to exit early

Format of names.json:

{
    "1": "Joe",
    "2": "Jane"
}

2. Train the Model

Run face_train.py to create the recognition model:

python src/face_train.py

• Processes all images in the images folder
• Creates a trained model file trainer.yml
• Shows number of faces trained

Note: Training images are saved as: Users-{id}-{number}.jpg

3. Run Face Recognition

Run face_recognizer.py to start real-time recognition:

python src/face_recognizer.py

• Your webcam will open and start recording
• Recognizes faces in real-time
• Shows name and confidence level
• Press 'ESC' to exit

Project Structure

├── src/
│   ├── config.yaml      # Configuration settings
│   ├── config.py        # Configuration loader
│   ├── face_taker.py    # Capture training images
│   ├── face_train.py    # Train the model
│   └── face_recognizer.py # Real-time recognition
├── images/              # Training images
├── names.json          # Name-ID mappings
└── trainer.yml         # Trained model