Implementation of a Face Recognition System Based on 2D-DCT and Modular 2DPCA

Implementation of a Face Recognition System Based on 2D-DCT and Modular 2DPCA (MATLAB)

Face recognition is a technology that identifies individuals by analyzing and comparing facial images. 2D-DCT (Two-Dimensional Discrete Cosine Transform) and Modular 2DPCA (Two-Dimensional Principal Component Analysis) are two commonly used feature extraction methods. The 2D-DCT algorithm transforms facial images into frequency-domain representations, extracting frequency-domain features through MATLAB's built-in dct2() function for efficient spectral analysis. Modular 2DPCA performs dimensionality reduction on facial images to extract spatial-domain features, implemented using covariance matrix computation and eigenvalue decomposition for optimal feature projection.

This research focuses on implementing a face recognition system based on 2D-DCT and Modular 2DPCA. Using MATLAB programming language, we develop a complete system comprising image preprocessing (including normalization and noise removal), feature extraction (implementing both transformation algorithms), and feature matching (using distance metrics like Euclidean or cosine similarity). The system architecture includes modular code organization with separate functions for each processing stage, ensuring maintainability and scalability. We validate the system's accuracy and performance using established face databases through comprehensive testing protocols including cross-validation and ROC curve analysis.

Through this implementation, we gain deeper understanding of 2D-DCT and Modular 2DPCA-based face recognition techniques, while contributing to advancements in facial recognition technology through optimized MATLAB implementations and benchmark performance evaluations.