Explore MATLAB source code curated for "人脸识别" with clean implementations, documentation, and examples.
Complete implementation programs for PCA (Principal Component Analysis) and KPCA (Kernel Principal Component Analysis), highly valuable for face recognition research with practical code examples and feature extraction methodologies
This article explores the application of manifold learning algorithms LLE (Locally Linear Embedding) and ISOMAP (Isometric Mapping) in face recognition systems, providing implementation insights and comparative analysis to assist developers in understanding dimensional reduction techniques for facial feature extraction.
This project demonstrates data dimensionality reduction using Discrete Cosine Transform combined with Principal Component Analysis, applicable to pattern recognition tasks like face recognition, palmprint analysis, expression classification, and fingerprint identification. The implementation involves signal transformation followed by feature extraction techniques.
PCA Face Recognition and Theoretical Framework (Updated version of Appendix_C_PCA_.rar with included MATLAB source code demonstrating practical implementation)
Face Recognition System: Implementing Principal Component Analysis (PCA) to distinguish between human faces and non-face objects. Primarily developed for a stochastic processes course project, featuring dimensionality reduction and pattern classification techniques.
PCA face recognition programs and essential materials. The programs are functional and have been personally tested by me, providing reliable code implementations for face recognition systems.
Implementation of PCA-based facial recognition in MATLAB, including solutions for handling large image file uploads and processing challenges.
MATLAB implementation of Principal Component Analysis (PCA) and its enhanced variant Multilinear PCA (MPCA) for face recognition applications, featuring complete source code with dimensionality reduction and feature extraction functionalities.
A comprehensive face recognition program developed in MATLAB, designed to assist with understanding fundamental principles and practical implementation
Explore the HoG SVM face recognition approach with code implementation insights - valuable for researchers studying facial recognition algorithms and their practical applications.