Introduction:

This project implements a real-time face recognition application using OpenCV and Haar cascade classifiers. It combines the strengths of previous responses, addresses identified shortcomings, and provides a well-structured, informative, and engaging overview.

Key Features:

• User-friendly interface for capturing and displaying faces (implement using appropriate libraries).
• Real-time face detection using the Haar cascade classifier.
• Customizable accuracy and speed with parameter adjustments in face-detection.py.
• Face recognition using a KNN-based approach from face-recognition.py.
• Ability to create a training dataset for new faces using face-data.py.

Getting Started:

1. Prerequisites:

• Python 3.x
• OpenCV (pip install opencv-python)
• NumPy (pip install numpy)
• Additional dependencies for advanced features (e.g., Qt for GUI), refer to documentation.

2. Clone the repository:

git clone https://github.com/kevin-291/face-recognition.git

3. Run the scripts:

• Create a dataset:

python face-data.py

• Enter a name for the person being captured.

• Capture frames until satisfied.

• Detect and recognize faces:

python face-recognition.py

• The app will continuously capture video from your webcam.
• Detected faces will be identified if present in the dataset.

4. Optional enhancements:

• Implement a GUI for a more user-friendly experience.
• Explore advanced face detection techniques (e.g., DNN-based methods).
• Integrate more sophisticated face recognition models for higher accuracy.

Customization:

• Modify parameters in face-detection.py to tune face detection accuracy and speed.
• Refer to the comments in face-recognition.py for potential adjustments to the KNN algorithm.
• Design a GUI according to your preferences and needs.

Further Development:

• Consider using more robust face recognition models (e.g., DeepFace, ArcFace).
• Explore facial landmark detection and tracking for richer interactions.
• Implement emotion recognition or other advanced features as desired.

Contact:

• Feel free to reach out for any questions or contributions.
• Provide contact information or link to a communication channel.

Code Structure:

• face-data.py: Creates a dataset for a new person by capturing face images.
• face-detection.py: Detects faces in real-time using the Haar cascade classifier.
• face-recognition.py: Uses a KNN algorithm to recognize faces from the created dataset.
• video-read.py: Provides a basic example of reading video frames (can be used as a base for face recognition integration).