Description: 笔者自行编写的Matlab环境下的人脸识别算法小实验，
采用的是经典的PCA算法，主要内容是采用SVD方法提取出象素空间上的
PCA主分量特征，分类算法上仅是基于最小距离分类
-The author of the Matlab environment to prepare the face recognition algorithm under small experiment using classical PCA algorithm, the main contents of SVD method is used to extract pixel space on the PCA principal component features, the only classification algorithm is based on the minimum distance classifier Platform: |
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Author:杨愚根 |
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Description: matlab编写的Modular PCA的源代码，以Yale人脸库为例。Moduplar PCA首先对原始图像分块，然后对分块后的所有子图像进行PCA分析提取投影特征，对待识别图像也是先进行分块，然后分别计算子图像在投影特征下的投影系数，最后根据最近邻分类器进行分类。-Modular PCA prepared matlab source code to Yale face database as an example. Moduplar PCA first block of the original image, and then on the block after all the sub-image analysis to extract PCA projection characteristics, treatment identification image is first block, and then calculated the characteristics of sub-image in the projector under the projection coefficient, and finally the light of recent Neighbor Classifier classification. Platform: |
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Author:章格 |
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Description: 人脸识别中PCA算法的matlab程序，使用最近邻分类器进行识别。-PCA Face Recognition Algorithm matlab procedures, the use of nearest neighbor classifier to identify. Platform: |
Size: 1024 |
Author:niushaobo |
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Description: 《机器学习》课上的作业，PCA和LDA降维，尽管网上很多，但很少注释，另外细节上也没注意。这里有很详细的注释。另外还附上一个Naive贝叶斯分类器，大家可以作比较。附带的图像包是OLR人脸。ReducedDim为想要提取的特征数，不是百分比！-" Machine learning" classes on the homework, PCA and LDA dimensionality reduction, even though a lot of online, but few notes, on the other did not pay attention to details. Here there is a very detailed notes. In addition, attach a Naive Bayesian classifier, we can compare. Fringe image packages are OLR people face. ReducedDim you want to extract the characteristics of a few, not a percentage! Platform: |
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Author: |
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Description: 机遇PCA的人脸识别，包括图像读取，PCA降维以及机遇简单贝叶斯分类-PCA Face Recognition opportunities, including the image read, PCA dimensionality reduction as well as the opportunity for easy Bayesian Classifier Platform: |
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Author:guodongyan |
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Description: PCA、LDA人脸检测，压缩包里实现了PCA、LDA、最小距离分类器，人脸检测！-PCA, LDA face detection, compression bag to achieve the PCA, LDA, minimum distance classifier, face detection! Platform: |
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Author:chen |
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Description: 这是基于PCA的人脸识别，用MATLAB编写，包含了K-L变换，奇异值分解等方法，且采用了最小距离分类器-This is based on the PCA face recognition, using MATLAB to prepare, including the KL transform, singular value decomposition and other methods, and the use of the minimum distance classifier Platform: |
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Author:pan |
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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: |
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Author:dong |
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Description: 本文设计基于DCT的人脸识别系统，首先结合当今人脸识别的背景和发展状况讨论了人脸识别的研究内容及在各方面的应用；然后研究了人脸识别进行预处理，讨论了人脸识别预处理的其他方法，分析各种方法的利弊，最后采用DCT（离散余弦变换）实现人脸图像预处理中的降维处理；接下来对人脸图像的特征提取进行了研究，简单叙述了几何特征提取和代数特征提取，同时深入研究了基于DCT和PCA变换的人脸图像特征提取，从而实现是否对人脸识别系统识别率有所提高的研究；对于分类器的选择，本文对两种分类器进行了探讨，即最近邻分类器和BP神经网络分类器，同时采用BP神经网络分类器作为本次基于DCT的人脸识别系统设计的分类器，并对BP神经网络进行分类的算法进行设计，BP神经网络具有学习功能，只要采用本系统对训练图片进行训练，就可以记下图像的相关信息，对于测试图片，就可以很准确的识别出该图片是属于哪个的。最后，本文对整个人脸识别系统设计实验进行了实验分析，实验结果表明本文采用的方法切实有效。- This is the design of the DCT-based face recognition systems. First of all, the background light of the current face recognition and face recognition to discuss the development of research in all aspects of content and applications And then studied the pretreatment of face recognition to discuss the pre-treatment of other face recognition methods,and analysis of the pros and cons of various methods, finally, the use of DCT (discrete cosine transform) image pre-processing to achieve in the face of the reduced-order processing Next on the face image feature extraction have been studied, A brief description of the feature extraction and algebraic geometry feature extraction, while in-depth study based on the DCT and the PCA Transform face image feature extraction in order to achieve face recognition system to identify whether the rate of increase in the research For the choice of classifier, this paper carried out on two of classifier, that is, nearest neighbor classifier and BP neura Platform: |
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Author:刘文珍 |
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Description: 此方法采用经典的PCA对人脸图像进行特征提取，用libsvm库函数的SVM分类器对图像分类。-This method uses the classical PCA on the face image feature extraction, with the libsvm library function of SVM classifier for image classification. Platform: |
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Author:zhangpei |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
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Author:mhm |
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Description: a code on pattern recognition which has pca as a dimentional reduction and knn as a classifier. Platform: |
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Author:fatal |
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Description: A basic PCA classifier is provided here for a two class classification problem.
An example is given, with some multimodal MRI scans from Multiple Sclerosis patients, in which the brain lesions of two patients are annotated and in the third are detected by the PCA model. Platform: |
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Author:AhmedMrc |
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Description: 我们使用MATLAB软件实现了人脸识别并统计其识别率。本实验采用PCA（主成分分析）方法，利用K-L变换和奇异值分解原理实现。并分别采用最近邻法分类器得出它们的成功率。-We use face recognition software and the MATLAB Statistics recognition rate. The present study, PCA (principal component analysis) method, using KL transform and singular value decomposition of implementation. The approach of using nearest neighbor classifier obtained their success rate. Platform: |
Size: 1024 |
Author:liyanliang163 |
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Description: The early detection of arrhythmia is very important
for the cardiac patients. This done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.-The early detection of arrhythmia is very important
for the cardiac patients. This is done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data. Platform: |
Size: 273408 |
Author:Amit Majumder |
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Description: 使用pca方法对图像进行特征提取，对训练集的20个人的共一百张人脸进行训练，使用adaboost算法生成强分类器，可以对测试集的人脸图片进行识别，且识别率较高(The PCA method is used to extract the features of the image, and the training is carried out for a total of 100 faces of 20 people in the training set. The AdaBoost algorithm is used to generate a strong classifier, which can recognize the face images in the test set with a high recognition rate) Platform: |
Size: 19835904 |
Author:王二愣子 |
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