2DLDA Algorithm for Face Recognition with MATLAB Implementation

This is a MATLAB program for face recognition that implements the 2DLDA (Two-Dimensional Linear Discriminant Analysis) algorithm and employs a nearest neighbor classifier for identification. The program processes input images through feature extraction and dimensionality reduction phases, then uses the trained nearest neighbor classifier to recognize facial images. The 2DLDA algorithm operates directly on image matrices without requiring vectorization, preserving spatial structure information while maximizing class separability through optimized projection matrices. Key implementation features include: - Direct matrix-based computation avoiding the vectorization step of traditional LDA - Efficient eigenvalue decomposition for optimal projection direction calculation - Nearest neighbor classification using Euclidean distance metrics - Automated training and testing pipeline with configurable parameters This algorithm has widespread applications in face recognition systems and can effectively identify identity information from facial images. Through this implementation, users can better understand and learn the practical implementation process of face recognition algorithms, enabling further research and improvements in pattern recognition and computer vision applications. The code provides clear modular structure for easy experimentation with different datasets and parameter configurations.