Traditional PCA-Based Algorithm for Face Recognition

In this study, we implemented face recognition using traditional Principal Component Analysis (PCA) methodology, with the Yale Face Database serving as our dataset. PCA operates as a statistical method rooted in Karhunen-Loève (K-L) transform, converting raw data into a new set of orthogonal variables termed principal components. Through principal component analysis, we identify which variables contribute most significantly to facial recognition. The K-L transform constitutes an optimal orthogonal transformation technique extensively applied in data compression domains. Our implementation involves computing eigenvectors from the covariance matrix of normalized face images, followed by dimensionality reduction through projection onto the principal component subspace. The recognition phase employs minimum distance classification (e.g., Euclidean or Mahalanobis distance) between test images and projected training templates. Experimental results demonstrate successful identification of faces within the Yale database, indicating substantial potential for PCA-based approaches in face recognition applications. Key implementation steps include image preprocessing, covariance matrix computation, eigenvalue decomposition, and feature vector matching through similarity measurement algorithms.