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Description: FISHERFACES FOR FACE RECOGNITION -FISHERFACES FOR FACE RECOGNITION
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Size: 308183 |
Author: 李琳莉 |
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Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.
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Size: 2513 |
Author: 小弟 |
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Description: Eigenfaces vs. Fisherfaces Recognition
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Size: 2200595 |
Author: 童灿 |
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Description: FISHERFACES FOR FACE RECOGNITION -FISHERFACES FOR FACE RECOGNITION
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Size: 308224 |
Author: 李琳莉 |
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Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.
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Size: 2048 |
Author: 小弟 |
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Description: Eigenfaces vs. Fisherfaces Recognition
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Size: 2200576 |
Author: 童灿 |
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Description: this is a document about eigen and fisherfaces
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Size: 281600 |
Author: mohamed baligh |
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Description: Tutorial to understans EigenFaces & FisherFaces. It has pseudo code for Matlab implementation of Eigenfaces & FisherFaces
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Size: 3688448 |
Author: Theo |
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Description: 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 matlab Face Recognition System Based on PCA and FLD-based face recognition systems, which sample the image of Essex face database for ' face94' part of images, documents may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. " procedures have been tested there is no problem to normal operation.
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Size: 377856 |
Author: 刘子木 |
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Description: FisherFace, an algorithm based on PCA and LDA
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Size: 3072 |
Author: Nguyen Viet Thang |
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Description: 线性判别分析(LDA)是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别
会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于I,DA的人脸识
别 文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fish—
erfaceS、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。-Low—dimensional feature representation with enhanced discriminatory power is of paramount importance to face
recognition(FR)system．Linear Discriminant Analysis(LDA)is one of the most popular linear classification techniques of
feature extraction。but it will meet two problems as computational challenging and “small sample size’’when applying to
face recognition directly．After studying people solve the two problems through several ways and realize the face recogni—
tion based on LDA． The short paper here makes compare on theory and experimental data analysis on several Face
Recognition system using LDA—Based Algorithm，such as Eigenfaces(using PCA)，Fisherfaces，DLDA，VDLDA and VD—
FLDA．The experimental results show that the VDFLDA method is the best of al1．
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Size: 222208 |
Author: 费富里 |
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Description: 用matlab编写的基于人脸识别系统,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-Prepared using matlab face recognition system based on the literature may refer to " Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection." Procedures have been tested there is no problem running
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Size: 501760 |
Author: jefferychang |
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Description: implementation of fisherfaces for matlab
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Size: 446464 |
Author: Botnet |
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Description: Face Recognition Based on FisherFaces
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Size: 27648 |
Author: killer |
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Description: opencv最新书籍《Master OpenCV with Practical Computer Vision Projects》。基于opencv2.4.3编写。采用了实例工程方式讲解。-opencv book:
Chapters:
Ch1) Cartoonifier and Skin Changer for Android, by Shervin Emami.
Ch2) Marker-based Augmented Reality on iPhone or iPad, by Khvedchenia Ievgen.
Ch3) Marker-less Augmented Reality, by Khvedchenia Ievgen.
Ch4) Exploring Structure from Motion using OpenCV, by Roy Shilkrot.
Ch5) Number Plate Recognition using SVM and Neural Networks, by David Escrivá.
Ch6) Non-rigid Face Tracking, by Jason Saragih.
Ch7) 3D Head Pose Estimation using AAM and POSIT, by Daniel Lélis Baggio.
Ch8) Face Recognition using Eigenfaces or Fisherfaces, by Shervin Emami.
Ch9) Developing Fluid Wall using the Microsoft Kinect, by Naureen Mahmood.
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Size: 6326272 |
Author: 王邦平 |
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Description: 两种人脸检测算法的论文：the Eigenface and the Fisherface-EIGENFACES AND FISHERFACES
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Size: 206848 |
Author: 胡冲 |
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Description: 人脸识别经典文献,介绍特征脸的核方法，核判别分析（KFDA）-Face classic literature, describes the characteristics of the face method, kernel discriminant analysis (KFDA)
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Size: 243712 |
Author: chensugen |
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Description: 本文在Matlab R2012a下面实现了fisherface算法，选用的人脸库是ORL，其中有40 人，每人有10幅不同的人脸图像。本文选取了每人9幅作为训练(1幅作为测 试)，图像大小为112x92。
主程序入口：Main.m
读取样本：CreatDatabase.m
FisherFace核心：FisherFaceCore.m
识别：Recognition.m
训练样本库：TrainDatabase
测试样本库：TestDatabase
不足：识别准确率有待提高。-In this paper, Matlab R2012a achieved fisherface following algorithm is chosen face database ORL, in which 40 people, each person will have 10 different facial images. This paper selected as the training 9 per person (one as a test), the image size is 112x92.
Main entrance: Main.m
Read sample: CreatDatabase.m
FisherFace Core: FisherFaceCore.m
Identification: Recognition.m
Training sample library: TrainDatabase
Test sample library: TestDatabase
Inadequate: recognition accuracy to be improved.
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Size: 4238336 |
Author: 邱竞 |
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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
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Size: 7168 |
Author: 迪迪 |
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Description: 基于Python的人脸检测与识别系统，用了特征脸方法和FisherFaces方法，都是业内最基础最经典的方案。-Python-based face detection and recognition system, using the methods and FisherFaces Eigenface method, the most basic is the industry s most classic scheme.
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Size: 5885952 |
Author: wt |
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