Explore MATLAB source code curated for "人脸库" 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.
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.
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.
Pre-processed ORL and YALE face database datasets ready for machine learning applications. When loaded using LODA, the 'train' variable contains training samples while 'test' represents testing samples, following standard dataset partitioning for model training and evaluation.
MATLAB-based image feature recognition techniques with classifier training approaches, comprehensive learning resources, and OpenCV integration for custom classifier development, including face database utilization for co-training.