Face Recognition System Based on MATLAB R2008

This document presents a face recognition system implemented in MATLAB R2008. The system employs two distinct algorithmic approaches: PCA (Principal Component Analysis) combined with Adaboost, and PCA integrated with SVM (Support Vector Machines). For training and testing, we utilized the ORL face database containing multiple facial images per subject. The system requires only a single facial image as input to successfully identify the individual, achieving a remarkable recognition accuracy of 84%.

The MATLAB R2008 implementation provides an efficient platform for facial feature processing, where PCA serves as the primary dimensionality reduction technique to extract essential facial features. The Adaboost implementation functions as a strong classifier by combining multiple weak classifiers, while the SVM component handles the classification task through optimal hyperplane determination in high-dimensional feature space. The ORL database's comprehensive collection of facial variations enables robust system performance across different lighting conditions and facial expressions.

This face recognition system demonstrates practical applicability in various domains including security surveillance, identity verification, and access control systems. The 84% recognition rate indicates substantial reliability for real-world deployments. The implementation provides not only an effective recognition methodology but also serves as a valuable research framework for further exploration and enhancement of facial recognition technologies.

In summary, the MATLAB R2008-based face recognition system represents a powerful and efficient solution for accurate facial identification. By leveraging the complementary strengths of PCA+Adaboost and PCA+SVM algorithms, combined with the comprehensive ORL database, the system achieves 84% recognition accuracy. This implementation opens new possibilities for facial recognition applications across multiple domains, providing a solid foundation for future technological advancements in biometric identification systems.