Face Recognition Project

This project implements face detection and recognition using a pre-trained FaceNet model and OpenCV. The application captures images from the webcam, detects faces in the captured images, and recognizes them based on a pre-built database of known faces.

Features

• Face Detection: Uses Haar Cascade to detect faces in images.
• Face Recognition: Recognizes faces using a pre-trained FaceNet model.
• Webcam Capture: Captures images directly from the webcam for face detection and recognition.
• Database Storage: Saves face signatures in a pickle file for future recognition.

Prerequisites

• Python 3.x
• The following Python libraries must be installed:
  • opencv-python
  • numpy
  • keras
  • tensorflow
  • Pillow
  • matplotlib

You can install the required libraries using pip:

pip install opencv-python numpy keras tensorflow Pillow matplotlib

Project Structure

.
├── fotoPeserta/        # Folder containing images of known participants
│   ├── participant1.jpg  # Example image for participant 1
│   ├── participant2.jpg  # Example image for participant 2
│   └── ...               # Additional images of known participants
├── facenet_keras.h5    # Pre-trained FaceNet model file
├── data.pkl             # Pickle file containing face signatures
├── Face_detection.ipynb  # Main Jupyter Notebook for face detection and recognition
└── README.md            # Project README file

How It Works

Face Detection: The notebook uses Haar Cascade Classifier to detect faces in the input images. Face Recognition: Detected faces are resized and pre-processed, then passed to the FaceNet model to generate face signatures. These signatures are stored in a database. Webcam Capture: The notebook captures images from the user's webcam and processes them for face detection and recognition. Output: Recognized faces are labeled with their corresponding identities.

Running the Project

Clone this repository: git clone https://github.com/SARATH-ANSIL/Face_recognition.git

Navigate to the project directory: cd Face-recognition

Ensure you have the required images of known participants in the fotoPeserta/ folder. Open the Jupyter Notebook:

jupyter notebook Face_recognition.ipynb

Run the cells in the notebook. Allow the application to access your webcam when prompted. Capture a photo, and the application will recognize any known faces.

Example Output

When running the notebook, if any faces are detected and recognized, the script will draw bounding boxes around the faces and label them with the participant's name.

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page for new ideas.

License

This project is licensed under the MIT License.

Acknowledgments

OpenCV for computer vision functionalities. Keras and TensorFlow for deep learning. Haar Cascades for face detection.