搜索资源列表
pca&mda
- pca，mda对ORL数据集上的人脸图像进行分类
facedata
- 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.
LDA-based-face-recognition
- 基于LDA的人脸识别技术，从ORL数据库读数据，然后从中取一部分进行训练，一部分进行测试。-This code is LDA base face recognition programme. It reads nots faces from ORL database and the rest (noc-nots) are used as test. LDA_Performance shows the recognition performance.
SVD
- 一个基于SVD分解的人脸识别程序，采用ORL数据集进行试验-An SVD decomposition program for face recognition using ORL data set to test
FastPCA_Feature_extraction-on-ORL
- 通过FastPCA在ORL人脸数据集上提取主成分脸并可视化，ReadFaces函数读取每个人的前五张图片作为实验数据集。-Extracting principal component faces on ORL_faces datasets by FastPCA and visualing them are done.ReadFaces function reads the five pictures of everyone as the experimental data set.
PCA_ORL
- 基于PCA和SVM的人脸识别系统的实现，实验数据采用ORL人脸库。-Based on the PCA and the SVM face recognition system is realized, the experiment data using ORL face database.
2DPCA
- 本程序采用2级PCA提取特征，最小藕欧距离分类器进行人脸识别，实验数据为orl人脸库。-failed to translate
KNN-Face-Recognition
- KNN分类算法实现人脸识别，数据集为ORL。训练样本分别为2、4、6，其余为测试样本。-KNN classification algorithm for face recognition, the data set for the ORL. 2,4,6 training samples respectively, the rest of the test samples.
PCAPLDA
- 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
ORL
- ORL人脸数据集，用于进行人脸识别，有40类人，每类人姿态，表情不同共10张-ORL face data set and used for face recognition, there are 40 kinds of people, each type of gesture, facial expression, a total of 10 different copies
orl
- orl人脸数据库用到深度学习工具箱之前的预处理，即将图像表达成字符串的形式。并将数据库一半分为测试数据，另一半训练数据。给出orl-Pretreat Orl face before using it in deep learning tool box, in the form of the image expressed by the string. Half are divided into test data, and the other half are training data.
faceRecognition
- 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
LBP-DBN-face-recognition-master
- 人脸识别，提出LBP和DBN方法的人脸识别算法，数据集是ORL人脸数据集。-Face recognition, face recognition algorithm LBP and DBN proposed method, the data set is ORL face dataset.
deeplearning_facerecognition
- 人脸识别，使用深度学习中的DBN算法进行人脸识别算法，数据集是ORL人脸数据集。-FACE Recognition, using the depth learning DBN algorithm recognition algorithm, the data set is a ORL face dataset.
滤波人脸识别
- 利用orl图像数据集，进行图像滤波，人脸检测。
]ORL+PCA+SVM-11
- 编写了用户界面程序实现ocr人脸数据集的识别，使用了svm分类器(A user interface program is developed to realize the recognition of OCR face data set, and the SVM classifier is used)
PCA+SVM
- 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))
PCA+SVM
- 采用经典的ORL人脸数据集，利用PCA进行进行降维，然后用SVM进行数据集的分类和训练。上传文件内包含libSVM3.2安装包(The classical ORL face dataset is used for dimension reduction by PCA, and then SVM is used to classify and train the dataset.)
ORL人脸数据库
- ORL人脸数据库，包含400幅人脸图像(40人， 每人1O幅， 大小为112像素x92像素)(ORL face database, including 400 face images (40 people, 10 for each person, 112 pixels x 92 pixels in size))