MATLAB Implementation of ICA-Based Face Recognition

I have developed an ICA-based face recognition program using MATLAB that achieves high recognition accuracy. The implementation includes core ICA algorithm functions (like FastICA or JADE) for feature extraction and statistical independence analysis of facial components. Beyond basic recognition functionality, I incorporated several preprocessing techniques through MATLAB's Image Processing Toolbox functions (imadjust, medfilt2) for image enhancement and noise reduction to ensure input quality. The computational efficiency was optimized using vectorization techniques and PCA dimensionality reduction prior to ICA processing. For model training, I utilized large-scale face datasets (like Yale ORL) with k-fold cross-validation to ensure robust generalization. The program structure includes modular functions for data loading, preprocessing, ICA training, and classification using distance metrics (Euclidean/cosine). Overall, this ICA implementation demonstrates not only accurate face recognition but also efficient performance and extensible architecture for additional feature integration.