Face Reconstruction using PCA

This project demonstrates how to reconstruct faces from the Yale Faces dataset using Principal Component Analysis (PCA). PCA is a statistical technique used to emphasize variation and capture strong patterns in a dataset. This project uses PCA to reconstruct faces by projecting the data onto principal components and creating a video to show the reconstruction process for a randomly selected image.

Project Structure

• reconstruct.ipynb: Jupyter Notebook containing the step-by-step implementation.
• reconstruct.py: Python script generated from the notebook.
• yalefaces/: Directory containing the dataset images.

Running the Project

Setup

To set up the environment, ensure you have the required libraries. You can install them using pip:

pip install numpy matplotlib pillow opencv-python

Using Jupyter Notebook

1. Open the reconstruct.ipynb file in Jupyter Notebook.
2. Run all cells to execute the code step-by-step.

Using the Python Script

1. Ensure you have the yalefaces directory in the same folder as the script.
2. Run the script using the following command:

python reconstruct.py

Expected Output

The project will generate a video file named reconstruction.avi, which demonstrates the step-by-step reconstruction of a randomly selected face from the dataset using PCA.

Future Work

• Implement more sophisticated reconstruction techniques.
• Enhance the project with additional datasets for a more comprehensive analysis.
• Include more examples and tutorials on PCA and its applications.

License

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

Contact

Farzan Mirza: farzanmrz@gmail.com | farzan.mirza@drexel.edu | LinkedIn