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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
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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
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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
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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
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自动人脸检测识别系统V5.0是一套集图象处理、人脸检测和人脸识别以及人脸图像数据库管理于一体的完整人脸识别系统。-Automatic Face Detection and Recognition System V5.0 is a set of image processing, face detection and face recognition and human face image database management in one complete face recognition
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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
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Copy all files in Matlab current directory and type "facerec" on
Matlab command window.
First, select an input image clicking on "Select image".
Then you can
- add this image to database (click on "Add selected image to database"
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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
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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
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This code is face localization programme. It reads faces from yale database and localize the face in the image.
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对人脸图像库的训练素材进行主成分分析，从而识别人脸图像。较详细说明见程序内部。-Face image database on the training material for the principal component analysis to identify the face image. See more details within the program.
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基于KL的人脸识别程序，但是缺少图像数据库，需要自己下载。-KL-based face recognition program, but the lack of image database, you need to download.
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First takes the last selected image Calls the images from database Applies PCA to images Compares the last selected image with database images and says if it is found at the database or not.
-First takes the last selected image Calls the images f
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This file takes cropped yale face images file as input and performs classifications based on
1. Correlation
2. Eigen vector based classification (Eigen Face)
3. Fisher Linear Discriminator (FLD) (Fisher Face)
The zip contains two files
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- Color provide an important clue for extracting the new color LBP histogram features for face recognition using Local Binary Pattern technique. To reduce redundancy RGB color space converted to YCbCr color space. The Local Binary Pattern is a non pa
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程序通过提取人脸图像的质心、协方差、偏转角等特征量与数据库中已有参数进行比对，从而进行人脸的识别。-Procedure to extract the centroid of the face image, covariance, the deflection angle has the feature quantity parameters of the database and compared to identify human faces.
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(1)Bayes分类
已知N=9， =3，n=2，C=3,问x= 应属于哪一类？
(2)聚类
使用c－均值聚类算法在IRIS数据上进行聚类分析
(3)鉴别分析
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。-(1) Bayes classification
Known N = 9, = 3, n = 2, C = 3, x = should ask which cat
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数据来源：ORL Database of Faces人脸数据库
其中同一个人的10张人脸图像为一组，共40组,图像大小为112x92
采用PCA算法实现对人脸的识别，当每组训练样本占70 时，识别准确率达到96.67 -Source: ORL Database of Faces face in which the same person 10 face images as a group, a total of 40 groups, image size of 112x92 using
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MATLAB课堂考勤（GUI）
该课题为基于MATLAB pca的人脸考勤系统。可以从一副图像中找出多人人脸，分割，计算人数，然后提前制作好这些人的人脸库，进行逐一识别是谁，是不是库内人脸，如是，具体是谁，如果不是，那提示库外人脸。具有友好的人机交互界面，还可以二次开发成摄像的，但是摄像头误差可能会有点。识别流程为：读取图像，人脸定位，人数统计，人脸分割，人脸识别，库内外判别。(The subject is face attendance system based on MATLAB PCA.
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