2DPCA: A Novel Dimensionality Reduction Method
2DPCA is an improved dimensionality reduction method based on traditional PCA, featuring innovative approaches and well worth exploring for enhanced data processing capabilities.
2DPCA Face Recognition Program with MATLAB Implementation
A MATLAB-based 2DPCA face recognition program achieving at least 10x faster processing speeds compared to traditional PCA methods while delivering superior recognition accuracy
MATLAB Source Code for Face Recognition Using Diagonal DCT and 2D PCA
MATLAB implementation for face recognition combining diagonal DCT feature extraction with 2D Principal Component Analysis algorithm.
MATLAB Implementation of 2DPCA Algorithm with Face Recognition Application
Implementation of 2DPCA algorithm in MATLAB using ORL face database, demonstrating high recognition accuracy through efficient feature extraction and dimensionality reduction techniques
2DPCA for Face Recognition, Gait Analysis, and Other Image Recognition Applications
2DPCA is applied to face recognition, gait analysis, and other image recognition tasks. It first normalizes irregular images by adjusting row and column proportions, then proceeds with training and recognition. The implementation includes comprehensive Chinese annotations and prompts, facilitating learning and reference for developers and researchers.
Classic Feature Extraction Algorithm: 2DPCA with MATLAB Implementation
A very classic feature extraction algorithm - 2DPCA source code written in MATLAB, featuring efficient matrix operations and dimensionality reduction techniques
Implementation of a Face Recognition System Based on 2D-DCT and Modular 2DPCA
MATLAB Implementation of a Face Recognition System Using 2D-DCT and Modular 2DPCA with Code Implementation Details
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.
Gabor 2DPCA Face Recognition Algorithm
A custom-implemented Gabor 2DPCA face recognition algorithm that extracts Gabor features and performs recognition using 2DPCA. Tested on the Yale face database with high accuracy and fast processing speed. The code allows direct recognition rate output by simply adjusting the number of training samples. Includes pre-loaded Yale database for immediate execution and result visualization - implements Gabor filter convolution, 2DPCA dimensionality reduction, and classification modules.