Face Recognition with PCA

Project Overview

This project explores the intersection of mathematics and ethics in facial recognition technology. By implementing Principal Component Analysis (PCA) based on the “Eigenfaces for Recognition” paper, I gained insights into both the mathematical foundations and the ethical considerations of biometric identification systems.

Key Aspects

• Implemented dimensionality reduction through PCA for facial feature extraction
• Explored the balance between recognition accuracy and computational efficiency
• Examined ethical implications of facial recognition technology
• Considered privacy concerns and representation issues in biometric systems

Interdisciplinary Value

This project bridges computer science, mathematics, and ethics, demonstrating how technical implementation must be guided by broader societal considerations. The work provided valuable insights into how seemingly abstract mathematical concepts directly influence systems that impact human privacy and identity.

Technologies Used

• Python for implementation with NumPy and OpenCV
• Statistical methods for feature extraction and analysis