Modified PCA Algorithm for Face Recognition - MATLAB Source Code

This documentation provides MATLAB source code implementing a modified PCA algorithm for face recognition. PCA (Principal Component Analysis) is a widely-used dimensionality reduction technique that preserves essential data characteristics while reducing computational complexity. In face recognition applications, PCA has become a standard approach for feature extraction and pattern matching. The provided code demonstrates how to implement PCA-based face recognition with performance enhancements through algorithm modifications - such as adjusting PCA parameters like eigenvalue thresholds, covariance matrix calculations, or experimenting with alternative dimensionality reduction techniques. The implementation includes key functions for data preprocessing, covariance matrix computation, eigenvalue decomposition, and projection into principal component space. By studying this source code, readers will gain deeper understanding of PCA applications in biometric systems and learn practical implementation techniques that can be adapted for their own data analysis projects, ultimately improving processing efficiency and recognition accuracy.