Explore MATLAB source code curated for "人脸识别" with clean implementations, documentation, and examples.
A comprehensive 2D-PCA-based face recognition and classification program featuring the complete FACE-ORL face database, with significant research and educational value, implementing dimensionality reduction and feature extraction techniques.
This face recognition program performs image preprocessing followed by feature extraction using Principal Component Analysis (PCA). The implementation includes histogram equalization, dimensionality reduction, and classification algorithms.
A MATLAB-based face recognition program I previously developed for feature extraction and identity matching, which implements core computer vision algorithms for practical applications.
Implementation of face recognition using traditional Principal Component Analysis (PCA) method on Yale Face Database. PCA is a statistical approach based on Karhunen-Loève (K-L) transform, which represents an optimal orthogonal transformation widely used in data compression applications.
A MATLAB-implemented face recognition system based on Markov Model and Support Vector Machine with integrated face database. This robust implementation (non-original) demonstrates excellent performance with key features: database generation from training/test samples; face recognition rate calculation (96.5 ); specific image identification; real-time camera-based face recognition. The system utilizes probability transition matrices for feature extraction and SVM classification for pattern recognition.
Face recognition system based on Principal Component Analysis (PCA) method, providing valuable reference for undergraduate graduation projects including complete source code and experimental images with detailed algorithm implementation
This experiment implements face recognition using Kernel Fisher Discriminant Analysis (KFDA) on the ORL face database, which contains 400 BMP images from 40 subjects with 10 images per person. The implementation includes feature extraction through kernel methods and linear discriminant optimization in high-dimensional space.
A significant unsupervised dimensionality reduction technique, this method serves as a linearized variant of the manifold learning algorithm Laplacian Eigenmaps, demonstrating exceptional performance in facial recognition applications. Implementation typically involves constructing adjacency graphs, computing Laplacian matrices, and solving generalized eigenvalue problems.
A MATLAB program for face recognition using PCA (Principal Component Analysis), achieving high recognition success rates through feature dimensionality reduction and eigenface computation techniques.
Complete PCA and Fisher face recognition code with detailed comments, ready for immediate implementation and customization.