搜索资源列表
flda
- fisher线性鉴别分析的人脸识别，在ORL库上实验，可在其他库上运行-fisher linear discriminant analysis for face recognition, in the ORL database on the test can be run on other database
ar_dct_kda
- 在AR人脸库上进行DCT变换，使用DCT变换后的图像进行 kernel fisher discriminant analysis,其中kernel 函数可以自己选择-In the AR face database on the DCT transform, using the DCT transformed image kernel fisher discriminant analysis, which can choose the kernel function
pca
- 运用奇异值分解定理的PCA方法在ORL人脸库上进行人脸识别，分类器为最近邻分类器,-The use of singular value decomposition theorem of PCA method in ORL face database for face recognition, nearest neighbor classifier for the classifier,
NPE
- 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embeddi
adaboostm1code
- 这是一篇关于adaboost.m1算法的人脸识别源码,识别率挺高的，读入任意人脸库即可运行，已经过测试-This is a source code of face recognition algorithm on adaboost.m1 .code has been tested ,you can read face database to run
yaleB01_P00.tar
- 人脸识别图片，找了很久才找到的，希望对大家有用-face database
FLDA
- 使用Fisher线性鉴别分析（FLDA）方法在ORL人脸数据库上进行人脸识别试验。ORL标准人脸库共包含40人，每人10幅共400幅BMP图像。-The use of Fisher linear discriminant analysis (FLDA) at Ways on ORL face database for face recognition test. Standard ORL face database contains a total of 40 people, 10 per pe
FLDbasedFaceRecognitionSystem_v2
- 基于fisherface的人脸图像识别，采用lda降维的方法来识别人脸图像-This package implements a well-known FLD-based face recognition method, which is called Fisherface [1]. All functions are easy to use, as they are heavy commented. Furtheremore, a sample scr ipt is incl
1
- Amir Hossein Omidvarnia用matlab编写的基于PCA的人脸识别系统和基于FLD的人脸识别系统,其中 的图像示例为Essex face database中 face94 的部分图像,文献可参考"Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection."已经测试过程序可正常运行没有问题。-Amir Hossein Omidvarnia prepared using
yaleB01_P02.tar
- face database人脸识别灰度图库，我想对做人脸识别的朋友会有帮助-face database
yaleB01_P04.tar
- face database人脸识别灰度图库，我想对做人脸识别的朋友会有帮助-face database
ar_dct_lda
- AR人脸库进行DCT变换，然后使用Fisher discriminant analysis 进行特征提取，使用cos分类器进行人脸分类。-AR Face Database for DCT transform, and then use the Fisher discriminant analysis feature extraction, using cos classifier for human face classification.
ar_dcv
- 在ar人脸库上使用鉴别公共向量方法（discriminant common vector）方法实现人脸图像鉴别分析-In the ar face database using the identification of public-vector method (discriminant common vector) method of achieving facial image discriminant analysis
2DLDAwiththeSVM-basedfacerecognitionalgorithm
- 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机 (SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽 略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸 识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem
KPCA
- 在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验. -Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and reco
Sparse_ar
- sparsity preserving projections (SPP)方法，根据论文《sparsity preserving projections with applications to face recognition》使用AR人脸数据库-sparsity preserving projections (SPP)method, according to the paper "sparsity preserving projections with applications t
faces
- face database for face recognition
SVM-face-recognition
- SVM face recognition 程序源代码 s1-s40 --------- 人脸数据库 face.m --------- 人脸识别主程序 load_database.m------载入数据函数 其他-------------LIBSVM工具箱函数 注意：LIBSVM需要手动安装，先安装编译器，再执行make.m-Source code S1-s40-----face database Face. M-----face recognition t
ICA-face-recognition
- 本程序是基于ICA（独立成分分析）方法进行人脸识别，人脸库已经给出，有需要的童靴自行下载-This procedure is based on ICA (independent component analysis) method for face recognition, face database has been given, there is a need to download children' s boots
Face-Recognition
- 通过主成分分析，对已有人脸库进行人脸识别，效果非常好，非常显著-Through principal component analysis, the existing face recognition, face database are effect is very good, is very significant