Facial Recognition System

Project Overview

This project aims to develop a facial recognition system capable of identifying and verifying faces in images or video streams. The system leverages advanced machine learning techniques and utilizes popular libraries such as OpenCV, Dlib. The project is divided into several components, including data preprocessing, face detection, feature extraction, model training, and real-time integration.

Project Structure

facial_recognition_system/
├── data/
│   ├── raw/                 # Raw data files (images)
│   ├── processed/           # Processed data (resized images, face encodings)
│   └── models/              # Trained models
├── src/
│   ├── data_preprocessing.py # Script for resizing images
│   ├── face_detection.py     # Script for detecting faces in images
│   ├── face_recognition.py   # Script for recognizing faces using encodings
│   ├── feature_extraction.py # Script for extracting face encodings
│   ├── model_training.py     # Script for training the face recognition model
│   ├── real_time_integration.py # Script for real-time face recognition
│   └── utils.py              # Utility functions
├── notebooks/
│   ├── EDA.ipynb             # Notebook for exploratory data analysis
│   └── Model_Training.ipynb  # Notebook for training the model
├── tests/
│   ├── test_data_preprocessing.py # Unit tests for data preprocessing
│   ├── test_face_detection.py     # Unit tests for face detection
│   ├── test_face_recognition.py   # Unit tests for face recognition
│   └── test_feature_extraction.py # Unit tests for feature extraction
├── requirements.txt           # List of project dependencies
├── README.md                  # Project documentation
└── setup.py                   # Setup script for packaging the project

Getting Started

Prerequisites

Ensure you have the following installed on your local machine:

• Python 3.8+
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/revanthchristober/Facial-Recognition-System.git
   cd Facial-Recognition-System

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Data Preprocessing:

   • Resize images in the data/raw folder:

   python src/data_preprocessing.py

2. Face Detection:

   • Detect faces in the preprocessed images:

   python src/face_detection.py

3. Feature Extraction:

   • Extract face encodings from the images:

   python src/feature_extraction.py

4. Model Training:

   • Train the facial recognition model using the extracted face encodings:

   python src/model_training.py

5. Real-Time Integration:

   • Use the trained model to recognize faces in real-time:

   python src/real_time_integration.py

Notebooks

• EDA.ipynb: Explore the dataset and perform data analysis.
• Model_Training.ipynb: Detailed steps for training the facial recognition model.

Tests

• Unit tests are provided in the tests directory. Run them using:

  pytest tests/

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

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

Acknowledgements

• OpenCV
• Dlib
• face_recognition
• scikit-learn