Principal Component Analysis-Based Face Recognition MATLAB Source Code

In this document, we explore a face recognition algorithm based on Principal Component Analysis (PCA). This algorithm utilizes Singular Value Decomposition (SVD) to extract principal components, which are then employed for face recognition. Specifically, the implementation converts facial images into numerical matrices and applies SVD decomposition to compute the principal component matrix, thereby reducing image dimensionality and complexity. The MATLAB code implements key functions including matrix normalization, covariance calculation, and eigenvalue sorting to optimize feature extraction. This approach enhances our understanding of facial patterns and improves face recognition accuracy through dimensionality reduction and feature selection. Additionally, the algorithm incorporates classification mechanisms using distance metrics (Euclidean or Mahalanobis) for face matching and can be applied to various domains such as security systems, biometric authentication, and automated identification platforms. The implementation includes functions for training set processing, projection coefficient calculation, and nearest-neighbor classification to ensure robust performance across different facial datasets.