PCA+Fisher: Applying Kernel Functions to Face Recognition Research
PCA combined with Fisher discriminant analysis integrates kernel functions for enhanced face recognition performance through nonlinear feature mapping.
MATLAB Implementation of ICA Algorithms for Process Monitoring
MATLAB Code Implementation of Independent Component Analysis with Industrial Applications
PCA+SVM Source Code Implementation
Source code implementation combining Principal Component Analysis (PCA) for dimensionality reduction and Support Vector Machine (SVM) for classification
ASM, Active Shape Models
ASM, Active Shape Models – Statistical Deformable Models for Shape Analysis
MATLAB Code Implementation for PCA Feature Extraction
MATLAB implementation of PCA feature extraction with code-level explanations
Face Recognition Using Eigenfaces
Implementation of face recognition using the eigenfaces method based on Principal Component Analysis (PCA)