Description: 从前用matlab编的人脸识别的特征提取以及识别程序，希望有用-The previous series of face recognition using matlab feature extraction and identification procedures, seek to help Platform: |
Size: 1024 |
Author:Bryan |
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Description: 基于非负矩阵分解（NMF）的人脸特征提取算法，NMF基本思想是找到一个线性子空间W，使的构成子空间的基本图像的像素点都是正值，而且人脸图像在子空间上的投影系数也是正数-Non-negative Matrix Factorization (NMF) of facial feature extraction algorithm, NMF basic idea is to find a linear sub-space W, so that the composition of sub-space of the basic image pixels are positive, and face image in the sub-space projection coefficient is positive Platform: |
Size: 1024 |
Author:李伟 |
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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 |
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Description: 特征提取是模式识别研究领域的一个热点.本文提出了一种基于独立成分分析和线性鉴别分析的特征提取方法.谊方法中引入了零空间的概念,指出了前人算法中的不足之,并且给出了一个完整的独立成分分析和线性鉴别分析的组合算法.在ORL和Yale人脸数据库上的实验表明了该方法的有效性.-Feature extraction is a hot field of pattern recognition research. In this paper, which is based on independent component analysis and linear discriminant analysis feature extraction method. Yi method introduced the concept of zero-space, pointing out that the algorithm predecessors shortcomings, and gives a complete independent component analysis and linear discriminant analysis of the combined algorithm. In the ORL and Yale face database, the experiment shows the effectiveness of this method. Platform: |
Size: 256000 |
Author:费富里 |
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Description: 模块pca, 在人脸识别中进行特征提取，速度效率比PCA要高，基于ORL人脸库上进行试验。-In face recognition module pca feature extraction by speed, efficiency, than pca based on ORL face database on the test.
Platform: |
Size: 3931136 |
Author:白万荣 |
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Description: 对于人脸数据库ORL，先用PCA进行特征提取，然后用SVM进行分类识别。里面有ORL数据库。-The ORL face database, first using PCA feature extraction using SVM for classification. There ORL database. Platform: |
Size: 231424 |
Author:rank |
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Description: 结合DCT和NNC进行人脸识别。先利用DCT提取特征，然后利用最近邻分类器分类，在ORL人脸库上测试效果不错。-The combination of DCT and NNC for face recognition. First DCT Feature Extraction, and then use the nearest neighbor classifiers, good test results on the ORL face database. Platform: |
Size: 1024 |
Author:尹贺峰 |
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Description: 结合DCT和BP神经网络进行人脸识别。先利用DCT提取特征，然后利用BP神经网络分类，在ORL人脸库上测试效果不错。-The combination of DCT and BP neural network for face recognition. First DCT Feature Extraction, and then use the BP neural network classifier, a good test results on the ORL face database. Platform: |
Size: 2048 |
Author:尹贺峰 |
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Description: 结合DCT和概率神经网络进行人脸识别。先利用DCT提取特征，然后利用PNN分类，在ORL人脸库上测试效果不错。-The combination of DCT and probabilistic neural network for face recognition. First DCT Feature Extraction, and then use a PNN classification, good test results on the ORL face database. Platform: |
Size: 1024 |
Author:尹贺峰 |
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Description: 提出了一种基于DCT提取人脸特征技术和支持向量机分类模型的人脸识别方法。利用离
散余弦变换可提取人脸可识别的大部分信息,而支持向量机作为分类器,在处理小样本、高维数等
方面具有独特的优势,且泛化能力很强,无需先验知识。从ORL 人脸库上的实验结果可以看出,
DCT特征提取是很有效的,且SVM的分类性能优于最近邻分类器,同时提高了整个系统的运算速
度。-A face recognition method based on DCT for face feature extraction and support vector machine classification model. Can extract most of the information face recognition using discrete cosine transform and support vector machine as classifier, has unique advantages in dealing with small sample, high dimension and generalization ability, without prior knowledge. As can be seen from the experimental results on the ORL database DCT feature extraction is very effective, and the SVM classification performance better than the nearest neighbor classifier, while increasing the speed of operation of the entire system. Platform: |
Size: 354304 |
Author:罗朝辉 |
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Description: 基于GABOR小波对ORL人脸库进行特征提取，程序简单，适合初学者-With GABOR wavelet feature extraction in ORL face database, procedure is simple to understand For beginners Platform: |
Size: 2048 |
Author:缴娇 |
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Description: 基于GABOR小波对ORL人脸库进行特征提取，程序简单，适合初学者-With GABOR wavelet feature extraction in ORL face database, procedure is simple to understand For beginners Platform: |
Size: 6144 |
Author:缴娇 |
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Description: Fisher线性鉴别分析已成为特征抽取的最为有效的方法之一 .但是在高维、小样本情况下如何抽取Fisher最优鉴别特征仍是一个困难的、至今没有彻底解决的问题 .文中引入压缩映射和同构映射的思想 ,从理论上巧妙地解决了高维、奇异情况下最优鉴别矢量集的求解问题 ,而且该方法求解最优鉴别矢量集的全过程只需要在一个低维的变换空间内进行 ,这与传统方法相比极大地降低了计算量 .在此理论基础上 ,进一步为高维、小样本情况下的最优鉴别分析方法建立了一个通用的算法框架 ,即先作K L变换 ,再用Fisher鉴别变换作二次特征抽取 .基于该算法框架 ,提出了组合线性鉴别法 ,该方法综合利用了F S鉴别和J Y鉴别的优点 ,同时消除了二者的弱点 .在ORL标准人脸库上的试验表明 ,组合鉴别法所抽取的特征在普通的最小距离分类器和最近邻分类器下均达到 97 的正确识别率 ,而且识别结果十分稳定 .该结果大大优于经典的特征脸和Fisherfaces方法的识别结果-Fisher linear discrimination analysis has become one of the most effective way to feature extraction, but in the case of high dimension and small sample how to extract Fisher optimal identification features is still a difficult, still hasn t completely solve the problem. In this paper, introducing the idea of compression mapping and isomorphism, ingeniously solved the high-dimensional theoretically, singular case to solve the problem of optimal identification vector set, and the whole process of the method to solve the optimal identification of vector set just in a low dimensional transformation space, compared with the traditional method greatly reduces the amount of calculation. Based on this theory, further to high dimension and small sample situation of optimal discrimination analysis method to establish the framework, a generic algorithm is first as K L transform, reoccupy Fisher identification transformations as a secondary feature extraction. Based on this algorithm framework, a Platform: |
Size: 7168 |
Author:迪迪 |
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Description: 人脸识别技术作为生物体特征识别技术的重要组成部分，在近些年来已经发展成为计算机视觉和模式识别领域的研究热点。本实验是基于K-L变换的主成分分析法（PCA）在人脸识别中的应用，在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真，并对样本图像进行了重构。本实验在ORL人脸库的基础上，选用每人前5张图片，共计40人200幅样本图像，通过快速PCA算法将10304维的样本特征向量降至20维，并实现了基于主分量的人脸重建，验证了PCA算法在高维数据降维处理与特征提取方面的有效性。-Facial recognition technology as a biological feature recognition technology is an important part of, in recent years has become a hot research topic in the field of computer vision and pattern recognition.This experiment is based on K- L transform principal component analysis (PCA) in the application of face recognition, based on ORL face validation of rapid PCA algorithm was realized by Matlab simulation, and reconstructed the sample image., on the basis of the experiment on ORL face , choose top 5 pictures each, a total of 40 people 200 sample image, through rapid PCA algorithm the sample feature vector of 10304 d down to 20 d, and implements the face reconstruction based on principal component, PCA algorithm is verified in the high-dimensional data processing and feature extraction is effective to dimension reduction. Platform: |
Size: 20067328 |
Author:季科 |
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Search - ORL feature extraction