Face Recognition System

This project is a Face Recognition System that allows users to add new faces to a dataset, train a classifier on the dataset, and recognize faces using a pre-trained classifier. It consists of three main Python scripts:

1. main.py - The entry point for the system, providing a menu for adding new users, training the classifier, and recognizing faces.
2. dataset_generator.py - Handles dataset generation by capturing images from the webcam, preprocessing them, and saving them for training.
3. face_recognizer.py - Recognizes faces using the trained classifier and displays the recognized name and confidence level on the screen.

Features

• Add New User: Capture images of a new user, add their details (name and ID) to the dataset, and update the classifier.
• Train Classifier: Automatically trains the classifier on the new dataset.
• Recognize Face: Uses the webcam to detect and recognize faces in real-time, displaying the recognized name on the screen.

Requirements

• Python
• OpenCV
• NumPy
• PIL (Pillow)

Setup and Installation

1. Clone the repository:

   git clone https://github.com/yourusername/facerecognition.git
   cd facerecognition

2. Install the required Python packages:

   pip install opencv-contrib-python numpy pillow

3. Download the Haar Cascade XML file for face detection:
   • You can download the haarcascade_frontalface_default.xml file from the OpenCv GitHub repository.
   • Place the file in the root directory of the project.
4. Run the program:

   python main.py
   

Project Structure

• main.py: The main script that provides a command-line interface for adding new users, training the classifier, and recognizing faces.
• dataset_generator.py: Handles the dataset generation and training of the classifier.
• face_recognizer.py: Performs real-time face recognition using the webcam.
• user_data.json: A JSON file that stores user data (name and ID) for recognition purposes.
• classifier.xml: The trained classifier file used for face recognition.

Usage

1. Add New User:

   • Run the script using python main.py.
   • Choose option 1 to add a new user.
   • Enter the name and ID of the user.
   • The system will guide you through capturing 30 images of the user's face. Follow the on-screen instructions.
   • The images will be saved, and the classifier will be trained automatically.

2. Recognize Face:

   • Run the script using python main.py.
   • Choose option 2 to recognize a face.
   • The system will use the webcam to detect and recognize faces in real-time, displaying the name and confidence level on the screen.

3. Exit:

   • Choose option 3 to exit the program.

JSON Data Storage

• The user data (name and ID) is stored in a user_data.json file as key-value pairs. The key is the ID, and the value is the name of the user.

Important Notes

• Ensure that your webcam is connected and working properly before running the program.
• The system requires good lighting conditions to perform accurate face recognition.
• The confidence level of recognition depends on the quality and number of images in the dataset.

License

This project is licensed under the MIT License.

Author

• Rajankumar Mandanka

Acknowledgments

• This project uses the OpenCV library for face detection and recognition.
• The Haar Cascade classifier used in this project is provided by OpenCV.