PCA Face Recognition Program Successfully Implemented

During my graduation project, my advisor provided me with a highly valuable program implementing face recognition using PCA (Principal Component Analysis) algorithm. The core implementation involved dimensionality reduction through eigenface calculation, where facial features were extracted by computing covariance matrices and eigenvectors from training datasets. I deeply appreciate my advisor for providing this program and offering comprehensive guidance throughout my implementation process. While working with the code, I encountered several technical challenges related to data preprocessing and eigenvalue decomposition, but my advisor consistently provided patient explanations helping me understand the algorithm's mathematical foundations and implementation nuances. The program ultimately ran successfully, incorporating key functions for image normalization, covariance matrix computation, and classification using distance metrics in the reduced feature space. This PCA implementation played a crucial role in my graduation project by demonstrating effective pattern recognition capabilities.