Face Recognition System Based on PCA and SVM (MATLAB Implementation)

This system is a MATLAB-implemented face recognition solution featuring fully executable source code. The implementation employs Principal Component Analysis (PCA) for dimensionality reduction of facial images, followed by Support Vector Machine (SVM) classification. The PCA algorithm extracts essential facial features by computing eigenvectors from the covariance matrix of training images, while SVM constructs optimal hyperplanes for robust classification. The system achieves high accuracy and stability in facial image recognition and verification tasks. Key MATLAB functions include pca() for feature extraction and fitcsvm() for classifier training. Suitable for various applications such as security access control systems, facial payment verification, and device unlocking mechanisms, this system demonstrates reliable performance across multiple scenarios. The code includes preprocessing modules for image normalization and feature scaling to enhance model generalization. We hope this comprehensive implementation proves valuable for your computer vision projects!