Implementation of Face Recognition System Using Principal Component Analysis (PCA)

In this article, we explore a face recognition system based on Principal Component Analysis (PCA). The implementation involves using the 2D PCA algorithm to compute a dimension reduction matrix that projects high-dimensional training set vectors into a lower-dimensional feature space. From a coding perspective, this typically involves calculating covariance matrices, performing eigenvalue decomposition, and selecting principal components based on variance thresholds. The system then applies the nearest neighbor classification method to evaluate recognition accuracy on test datasets, where distance metrics (such as Euclidean or Mahattan distance) are computed between projected test samples and training templates. This methodology demonstrates significant applicability in image processing and face recognition domains due to its computational efficiency and accuracy in handling large-scale datasets. The algorithm's effectiveness stems from its ability to capture essential facial features while eliminating redundant information through orthogonal transformation.