Explore MATLAB source code curated for "ORL人脸数据库" with clean implementations, documentation, and examples.
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.
Face recognition experiments using Fisher Linear Discriminant Analysis (FLDA) method on the ORL face database. The ORL standard face database contains 40 subjects with 10 images each, totaling 400 BMP format images. FLDA implementation involves feature extraction and dimensionality reduction to maximize inter-class separation while minimizing intra-class variation.
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.
The ORL Face Database is a valuable resource for face recognition tasks, containing 400 images from 40 subjects with 10 images per person - suitable for implementing recognition algorithms using techniques like PCA, LDA, or deep learning approaches.