This project is a sophisticated doorlock system that integrates real-time face recognition using a Convolutional Neural Network (CNN) model. The system captures face images, preprocesses the data, trains a CNN model, and performs real-time face recognition to grant access. This project enhances security and provides a modern, hands-free access control solution.

• Real-time Face Recognition: Uses a trained CNN model for identifying faces.
• Image Data Collection: Captures face images using a webcam or IP camera.
• Data Preprocessing: Processes and prepares the image data for training.
• Model Training: Trains a CNN model on collected face data.
• Secure Access Control: Grants or denies access based on face recognition results.

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

   git clone https://github.com/your-username/doorlock-system.git
   cd doorlock-system
   

2. Create Virtual Environment: python3 -m venv venv source venv/bin/activate

3. Install Dependencies: pip install -r requirements.txt

4. Download and Place Haarcascade File: Download the haarcascade_frontalface_default.xml from OpenCV GitHub and place it in the model directory.

1. Data Collection: Run the data collection script to capture face images:

   python model/data_collection.py

   Follow the on-screen instructions to capture and save images.

2. Data Preprocessing:

   Run the data preprocessing script:

   python model/data_preprocessing.py

3. Model Training:

Train the CNN model on the preprocessed data:

python model/train_model.py

4. Real-time Face Recognition:

   Run the face recognition script to start real-time face recognition:

   python model/face_recognition.py

doorlock-system/ ├── backend/ ├── data/ │ ├── images/ │ └── clean_data/ ├── doorlock/ ├── frontend/ ├── model/ │ ├── data_collection.py │ ├── data_preprocessing.py │ ├── train_model.py │ ├── face_recognition.py │ └── final_model.h5 ├── scripts/ ├── 3Dmodels/ ├── requirements.txt └── README.md

Contributions are welcome! To contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

1. Thanks to the OpenCV community for providing the Haar Cascade classifier.
2. Special thanks to all contributors and collaborators.

This README.md file provides a comprehensive overview of your project, including installation and usage instructions, which will help other users and contributors understand and work with your doorlock system project.