这是读好的ORL 和YALE人脸库数据， 用LODA加载后， 变量train 代表是训练样本， test 代码是测试样本。-This is a good time ORL and Yale face database data, using Loda loaded, variable train representatives training samples, test code is to test the samples.

PCA+LDA人脸识别，PCA降维到N-C,（N为训练样本数，C为类别数）使得Sw非奇异，主要是解决小样本，数据集为ORL，每类取9（可改）个图片-PCA+LDA recognition, PCA dimensionality reduction to NC, (N is the number of training samples, C is the number of categories) make Sw nonsingular, mainly to resolve the small s

基于SVM和PCA的人脸识别，使用了ORL人脸数据集和libsvm.jar-Face recognition based on SVM and PCA. ORL faces dataset and libsvm.jar are used

用于人脸识别，包含了PCA及SVM算法，数据集采用的ORL数据库(face recognition(PCA+SVM))