Explore MATLAB source code curated for "人脸数据库" with clean implementations, documentation, and examples.
A comprehensive face recognition system based on Local Binary Pattern (LBP) methodology, featuring subroutines for local binarization and histogram feature vector extraction. The system supports integration with face databases for testing recognition accuracy and demonstrates high recognition rates through robust algorithmic implementation.
YALE Face Database contains 15 subjects with 11 facial images per person, providing robust data for developing and testing face recognition algorithms through various lighting conditions and expressions.
The Yale B database is one of the most commonly used facial databases for illumination preprocessing research in face recognition. This dataset contains pre-cropped frontal facial images from 10 subjects, with 64 images per person totaling 640 images. The images are ready for immediate use in research implementations, typically requiring basic Python/Matlab code for loading and normalization such as OpenCV's imread() or PIL's Image.open() functions with reshape operations to standardize dimensions.
SVM face recognition program source code includes s1-s40 face database, face.m as main recognition program, and load_database.m for data loading function. Note: LIBSVM toolbox functions require manual installation - first install compiler, then execute make.m for setup.