MATLAB Face Recognition Code Using Principal Component Analysis (PCA)

In this project, we implement face recognition functionality using MATLAB programming with Principal Component Analysis (PCA) as the core algorithm. PCA is a widely-used dimensionality reduction technique that projects high-dimensional data into lower-dimensional space to extract the most representative features. Our implementation involves writing MATLAB code to perform eigenface computation using covariance matrix decomposition and eigenvalue analysis. The code will include functions for data preprocessing, such as image normalization and noise reduction, to enhance algorithm robustness. During implementation, we will focus on code readability and maintainability through proper function modularization and documentation. We will conduct thorough testing and optimization using standard face datasets to ensure accuracy and computational efficiency. The project will utilize MATLAB's built-in matrix operations and statistical tools for efficient PCA computation. We believe this project will provide deep understanding of face recognition principles and applications, delivering excellent results in practical implementations.