2DLDA Algorithm for Face Recognition

example: Complete demonstration program that integrates all modules for end-to-end face recognition system implementation. creatData: Function that processes raw facial images and converts them into standardized data matrices suitable for LDA processing, typically handling image normalization and formatting. creatTrainLabelMat: Creates categorical label matrices that map each training sample to its corresponding class identity, essential for supervised learning. generateTestData: Generates test dataset matrices separate from training data, ensuring proper evaluation protocol with unseen samples. generateTestLabelMat: Produces label matrices for test data to enable performance measurement and accuracy calculation. LDA: Implements 2D Linear Discriminant Analysis (Fisherface) algorithm that extracts discriminative features while maximizing between-class variance and minimizing within-class variance, operating directly on image matrices without vectorization. knnRecognition: K-Nearest Neighbors classifier implementation that compares test features against training features using distance metrics (typically Euclidean) and performs majority voting for classification decisions. knnsearch: Optimized nearest neighbor search function that efficiently finds k closest matches in feature space, often utilizing tree-based structures for faster retrieval in high-dimensional spaces.