PCA-Based Face Recognition Method MATLAB Source Code

In this document, we present a PCA-based face recognition method, which represents a significant technique in the computer vision field. This approach utilizes Principal Component Analysis (PCA) technology to reduce dimensionality in facial images, thereby improving image recognition accuracy. To facilitate better understanding of this methodology, we provide MATLAB source code named "PCA_based Face Recognition System.rar" for reference and practical implementation. The implementation typically involves several key algorithmic steps: first, preprocessing facial images by converting them to grayscale and normalizing dimensions; second, constructing a covariance matrix from the training dataset; third, computing eigenvectors and eigenvalues to identify principal components; and finally, projecting test images onto the PCA subspace for classification using distance metrics like Euclidean or Mahalanobis distance. This program not only helps users comprehend PCA principles and applications but also enables practical implementation of face recognition functionality. The code includes functions for data preprocessing, eigenface calculation, feature vector projection, and recognition comparison. We believe this PCA-based face recognition approach will see broader applications and development in future computer vision systems.