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用主成分分析与神经网络进行人脸的识别
文件是整个的ＭＡＴＬＡＢ数据文件-using principal component analysis and neural networks face identification document is the entire data file MATLAB
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PCA---主成分分析 LDA---线性区别分析此类实现结合两者的有缺点实现图像模式识别,其中需要有矩阵类-PCA principal component analysis --- --- LDA linear discriminant analysis combining the two to achieve such a flawed it Image is pattern recognition, which requires matrices
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结合PCA+LDA的图像识别算法VC封装类,PCA（主元素分析，光照敏感）,可用于人脸识别的初级算法-combination of image recognition algorithm VC Packaging category, PCA (principal component analysis, Light-sensitive), can be used for the initial face recognition algorithm
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用于人脸识别的主成分分析和模糊支持向量机程序，通过测试，能完成工作,Face Recognition for the principal component analysis and fuzzy support vector machine procedures, to pass the test to complete the work
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Face Recognition, Face Detection, Lausanne Protocol, 3D Face Reconstruction,
Principal Component Analysis, Fisher Linear Discriminant Analysis,
Locality Preserving Projections, Kernel Fisher Discriminant Analysis,Face Recognition, Face Detection, L
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用于人脸识别的模糊独立成分分析+主成分分析，用模糊支持向量机进行的分类。,Fuzzy Face Recognition for independent component analysis+ principal component analysis, using fuzzy support vector machine classification.
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基于主成份分析(PCA)的人脸识别算法MATLAB程序的实现。机器视觉的作业，内附人脸识别的matlab程序，和人脸库，还有作业的详细要求，以及格式示例和部分参考文献。-Based on principal component analysis (PCA) of the face recognition algorithm MATLAB program implementation. Machine vision operations, included face recognition mat
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人脸识别matlab程序，pca主成分分析法，采用最小距离决策-Face recognition matlab program, pca principal component analysis method, using a minimum distance between the decision-making
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人脸识别：使用PCA方法，即主成分分析，区分人脸和非人脸。主要用于随即过程大作业。-Face Recognition: Using the PCA method, that is, principal component analysis, the distinction between face and non-human face. Then the process used mainly for large operations.
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人脸识别，主成分分析，模式识别，图像处理-Face recognition, principal component analysis, pattern recognition, image processing
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图像处理，人脸识别，模式识别，主成分分析-Image processing, face recognition, pattern recognition, principal component analysis
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基于pca主成分分析实现人脸识别功能，是人脸识别中比较经典的算法之一，取得很好的效果-Pca principal component analysis based on the realization of face recognition function, is face recognition algorithm to compare the classical one, and achieved good results
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基于核函数的主分量分析法源代码，可用于人脸识别-Kernel-based principal component analysis source code, can be used for face recognition
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基于主分量分析法的脱机手写数字识别
文章(pdf格式)-Based on principal component analysis of the off-line handwritten numeral recognition article (pdf format)
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基于主分量分析法的脱机手写数字识别
文章 (pdf格式)-Based on principal component analysis of the off-line handwritten numeral recognition article (pdf format)
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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 influen
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基于改进二维主成分分析的在线掌纹识别
能有效的识别掌纹信息-Can effectively identify palmprint information
Two-dimensional principal component analysis based on improved on-line palmprint identification
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在matlab中实现主成分分析方法，并使用FERET库的灰度图像进行测试-This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database.
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用于特征降维人脸识别等多元数据分析的主分量分析投影的Matlab代码实现。-For feature reduction and other multivariate data analysis, face recognition principal component analysis projection of the Matlab code implementation.
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matlab主成分分析，含完整主成分分析方法以及matlab源代码-matlab principal component analysis, including principal component analysis and the complete source code matlab
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