PCA+Fisher Face Recognition MATLAB Implementation
MATLAB program for PCA+Fisher face recognition with included image database, suitable for introductory facial recognition projects with algorithm implementation details.
PCA Dimensionality Reduction Method for Pattern Classification
In pattern classification tasks such as fingerprint recognition and facial recognition, handling high-dimensional data presents significant challenges - facial data often contains millions of dimensions, exceeding current computational capabilities for rapid processing. PCA (Principal Component Analysis) serves as an effective dimensionality reduction technique that projects high-dimensional data into a lower-dimensional subspace while preserving essential variance patterns.
Face Recognition Implementation Using PCA+KNN Algorithm with 2DPCA Approach
Implementation of face recognition using PCA+KNN algorithm with 2DPCA-based methodology, offering reduced computational time and enhanced efficiency through matrix-based feature extraction.
Face Recognition Experiment Using 2DPCA and 2DLDA on ORL Face Database
Implementation of face recognition experiments using 2DPCA and 2DLDA algorithms on the ORL face database, featuring detailed code annotations ideal for beginners. This project provides practical understanding of PCA and LDA algorithms and their application in computer vision.
Comprehensive Implementation of Major Manifold Learning Methods
This code repository contains implementations of prevalent manifold learning algorithms including PCA, ISOMAP, LLE, and HLLE with detailed execution examples and parameter configurations.
MATLAB Implementation of Non-Negative Matrix Factorization
Non-Negative Matrix Factorization is a novel subspace decomposition method that incorporates non-negativity constraints, proving more effective than traditional PCA and ICA approaches for certain applications
PCA-Based Facial Expression Recognition
Facial expression recognition system based on Principal Component Analysis (PCA) capable of identifying three emotional states: happiness, anger, and disgust, with implementation details for feature extraction and classification.
Gabor Wavelet Face Recognition with Feature Extraction and Dimensionality Reduction
Gabor wavelet-based face recognition system utilizing LBP feature extraction, PCA, and LPP dimensionality reduction. A graduation project focusing on algorithm implementation for optimized facial recognition under challenging conditions.
PCA+SVM Source Code Implementation with MATLAB
MATLAB source code implementing PCA for feature extraction and SVM for classification - Complete workflow with dimensionality reduction and machine learning algorithms
PCA Program for Face Recognition with Implementation Details
The PCA program for face recognition demonstrates excellent performance in MATLAB environment with efficient code implementation