Face Detection and Recognition Project

Table of Contents

• Introduction
• Technologies Used
• Installation
• Project Structure
• Usage
• Contributing

Introduction

This project aims to provide a real-time face detection and recognition system. It captures face images, encodes them, and then uses these encodings for face detection and recognition.

Technologies Used

• Python 3.x
• OpenCV
• Dlib
• NumPy
• Jupyter Notebook

Installation

1. Clone the repository

   git clone https://github.com/your_username/Face_Detection_and_Recognition.git

2. Navigate to the project directory

   cd Face_Detection_and_Recognition

3. Install the required packages

   pip install opencv-python
   pip install face_recognition

Project Structure

Face_Detection_and_Recognition/
├── README.md
├── requirements.txt
├── captured_images/                  # Directory for storing captured face images
├── src/
│   ├── capturing_images.py           # Script for capturing face images using the camera
│   ├── encodings_creator.py          # Script for creating encodings from captured images
│   └── face_detection_and_verification.ipynb  # Jupyter Notebook for live face detection and recognition
└── data/
    └── known_faces.pkl               # Serialized encodings of known faces

File Descriptions

• capturing_images.py: Captures face images using the camera and stores them in the captured_images/ directory.
• encodings_creator.py: Reads images from the captured_images/ directory, encodes the faces, and stores the encodings in known_faces.pkl.
• face_detection_and_verification.ipynb: Contains the main logic for real-time face detection and recognition. Uses the encodings stored in known_faces.pkl.
• known_faces.pkl: Serialized file that contains the encodings of known faces. Generated by encodings_creator.py.

Usage

1. Run capturing_images.py to capture face images.

   python src/capturing_images.py

2. Run encodings_creator.py to create face encodings.

   python src/encodings_creator.py

3. Open face_detection_and_verification.ipynb in Jupyter Notebook and run the cells for live detection and recognition.

Contributing

To contribute to this project, please fork the repository and submit a pull request.