Face Recognition Using Two-Dimensional Linear Discriminant Analysis

The face recognition method utilizing Two-Dimensional Linear Discriminant Analysis has been comprehensively tested on the ORL face database to evaluate its recognition accuracy and performance metrics. The experimental results demonstrate that this approach achieves outstanding performance in face recognition tasks, accurately identifying individuals within the ORL database through optimized feature extraction and dimensionality reduction techniques. This 2D-LDA based methodology implements direct image matrix processing without requiring vectorization, preserving spatial structural information through between-class and within-class scatter matrix calculations. The algorithm computes optimal projection vectors that maximize class separability while minimizing intra-class variations, typically implemented using eigenvalue decomposition or generalized singular value decomposition (GSVD) techniques. The method exhibits not only high recognition rates but also demonstrates remarkable robustness and stability against variations in lighting conditions, facial expressions, and minor occlusions. Its computational efficiency and structural preservation capabilities make it suitable for various practical applications including security systems, biometric authentication, and automated surveillance platforms. The implementation typically involves preprocessing steps like image normalization, followed by 2D-LDA transformation matrix computation, feature projection, and classification using distance metrics like Euclidean or Cosine similarity.