Face Feature Extraction and Recognition Implementation Using SVM

This project implements face feature extraction and recognition using Support Vector Machine (SVM) algorithm developed in MATLAB. The implementation involves several key stages: preprocessing facial images, extracting discriminative features using techniques like PCA (Principal Component Analysis) or LBP (Local Binary Patterns), and training SVM classifiers with optimized kernel functions. After completing the algorithm implementation, we conduct comprehensive performance analysis to evaluate the system's accuracy and efficiency. The recognition pipeline includes feature normalization, SVM model training with cross-validation, and classification of test images using predictive functions. By extracting and recognizing facial features, this technology can be applied to various practical applications such as face recognition access control systems, facial expression analysis, and biometric authentication. This research makes significant contributions to the advancement of face recognition technology and demonstrates promising potential for real-world deployment through systematic MATLAB coding practices and rigorous experimental validation.