Description: 二维的LDA+PCA人脸识别matlab程序，都可以直接使用。-Two-dimensional LDA+ PCA face recognition matlab procedures can be used directly. Platform: |
Size: 2808832 |
Author:风铃 |
Hits:
Description: （压缩包里一共有5个代码）
pca+lda+粗糙集+模糊神经网络
saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat
1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat
2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat
3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat
4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat
5.用runfnn.m对rstest进行测试得到其识别率
savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.-pca+ lda+ Rough Set+ fuzzy neural network
saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat
Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat
2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat
3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat
4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat
5. Rstest used to test for runfnn.m by its recognition rate
cm.m is savem.m and minimum distance classifier on the training set and test set classificati Platform: |
Size: 2048 |
Author:dong |
Hits:
Description: 实现2DLDA和LDA对人脸识别的比较，有论文和相应的程序，matlab编写-A matlab code of 2DLDA and LDA for face recognition Platform: |
Size: 288768 |
Author:xiaoxiran |
Hits:
Description: 摘要：主成分分析（PCA）的人脸识别算法，以减少的特征向量是涉及到对抽象的特点，改进了主成分分析（一）iUumination算法的变化影响酶原sed.The方法是基于上减低与正常化其相应的标准差的特征向量元素相关联的大特征值的特征向量的影响力的想法。耶鲁大学和耶鲁大学面临的数据库面对数据库B是用来验证-Abstract：In principal component analysis(PCA)algorithms for face recognition，to reduce the influence of the
eigenvectors which relate to the changes of the iUumination on abstract features，a modified PCA ( A)
algorithm is propo sed．The method is based on the idea of reducing the influence of the eigenvectors associated
with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation．
Th e Yale face database and Yale face database B are used to verify the method．The simulation results show
that，f0r front face and even under the condition of limited variation in the facial po ses the proposed method
results in better perform ance than the conventional PCA and linear discriminant analysis(LDA)approaches．and
the computational cost remains the same as that ofthe PCA，and much less than that ofthe LDA． Platform: |
Size: 205824 |
Author:费富里 |
Hits:
Description: 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 of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition． Platform: |
Size: 236544 |
Author:费富里 |
Hits:
Description: 利用2DPCA和2DLDA在ORL人脸数据库上做的人脸识别实验，注解详细，适合初学者使用，对理解PCA与LDA有很大的作用。-2DPCA and 2DLDA use in ORL face database for face recognition experiments do, comment in detail, suitable for beginners, on the understanding of PCA and LDA are very useful. Platform: |
Size: 3731456 |
Author:xiaocai |
Hits:
Description: 在这篇文章中，我们主要阐述了基于PCA和LDA的人脸识别技术。这个技术包含两个步骤：首先，我们通过PCA将人脸图片从原始向量空间中提取到子向量空间——特征脸空间；然后，再通过LDA获得一个线性分类器。-In this article, we mainly elaborated based on PCA and LDA face recognition technology. This technique consists of two steps: First, we will face images extracted from the original vector space to sub-vector space by PCA- Eigenface space Then, LDA obtained by a linear classifier. Platform: |
Size: 28672 |
Author:zhengluping |
Hits:
Search - face recognition by lda