哪位好心人可以提供一份LDA人脸识别的matlab程序啊？谢谢了！

以下是宏者LDA的m文件函数：

你稍稍改改就能用了！

function [eigvector, eigvalue, elapse] = LDA(gnd,options,data)

% LDA: Linear Discriminant Analysis

%

% [eigvector, eigvalue] = LDA(gnd, options, data)

%

% Input:

% data - Data matrix. Each row vector of fea is a data point.

% gnd - Colunm vector of the label information for each

% data point.

% options - Struct value in Matlab. The fields in options

% that can be set:

%

%Regu - 1: regularized solution,

%a* = argmax (a'X'WXa)/(a'X'灶正Xa+ReguAlpha*I)

% 0: solve the sinularity problem by SVD

% Default: 0

%

% ReguAlpha - The regularization parameter. Valid

% when Regu==1. Default value is 0.1.

%

%ReguType - 'Ridge': Tikhonov regularization

% 'Custom': User provided

% regularization matrix

% Default: 'Ridge'

%regularizerR - (nFea x nFea) regularization

% matrix which should be provided

% if ReguType is '隐绝悔Custom'. nFea is

% the feature number of data

% matrix

%Fisherface - 1: Fisherface approach

% PCARatio = nSmp - nClass

% Default: 0

%

%PCARatio - The percentage of principal

%component kept in the PCA

%step. The percentage is

%calculated based on the

%eigenvalue. Default is 1

%(100%, all the non-zero

%eigenvalues will be kept.

%If PCARatio >1, the PCA step

%will keep exactly PCARatio principle

%components (does not exceed the

%exact number of non-zero components).

%

%

% Output:

% eigvector - Each column is an embedding function, for a new

% data point (row vector) x, y = x*eigvector

% will be the embedding result of x.

% eigvalue - The sorted eigvalue of LDA eigen-problem.

% elapse- Time spent on different steps

%

%Examples:

%

% fea = rand(50,70)

% gnd = [ones(10,1)ones(15,1)*2ones(10,1)*3ones(15,1)*4]

% options = []

% options.Fisherface = 1

% [eigvector, eigvalue] = LDA(gnd, options, fea)

% Y = fea*eigvector

%

%

% See also LPP, constructW, LGE

%

%

%

%Reference:

%

% P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, 揈igenfaces

% vs. fisherfaces: recognition using class specific linear

% projection,� IEEE Transactions on Pattern Analysis and Machine

% Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.

%

% Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, and Thomas Huang,

% "Learning a Spatially Smooth Subspace for Face Recognition", CVPR'2007

%

% Deng Cai, Xiaofei He, Jiawei Han, "SRDA: An Efficient Algorithm for

% Large Scale Discriminant Analysis", IEEE Transactions on Knowledge and

% Data Engineering, 2007.

%

% version 2.1 --June/2007

% version 2.0 --May/2007

% version 1.1 --Feb/2006

% version 1.0 --April/2004

%

% Written by Deng Cai (dengcai2 AT cs.uiuc.edu)

%

if ~exist('data','var')

global data

end

if (~exist('options','var'))

options = []

end

if ~isfield(options,'Regu') | ~options.Regu

bPCA = 1

if ~isfield(options,'PCARatio')

options.PCARatio = 1

end

else

bPCA = 0

if ~isfield(options,'ReguType')

options.ReguType = 'Ridge'

end

if ~isfield(options,'ReguAlpha')

options.ReguAlpha = 0.1

end

end

tmp_T = cputime

% ====== Initialization

[nSmp,nFea] = size(data)

if length(gnd) ~= nSmp

error('gnd and data mismatch!')

end

classLabel = unique(gnd)

nClass = length(classLabel)

Dim = nClass - 1

if bPCA &isfield(options,'Fisherface') &options.Fisherface

options.PCARatio = nSmp - nClass

end

if issparse(data)

data = full(data)

end

sampleMean = mean(data,1)

data = (data - repmat(sampleMean,nSmp,1))

bChol = 0

if bPCA &(nSmp >nFea+1) &(options.PCARatio >= 1)

DPrime = data'*data

DPrime = max(DPrime,DPrime')

[R,p] = chol(DPrime)

if p == 0

bPCA = 0

bChol = 1

end

end

%======================================

% SVD

%======================================

if bPCA

if nSmp >nFea

ddata = data'*data

ddata = max(ddata,ddata')

[eigvector_PCA, eigvalue_PCA] = eig(ddata)

eigvalue_PCA = diag(eigvalue_PCA)

clear ddata

maxEigValue = max(abs(eigvalue_PCA))

eigIdx = find(eigvalue_PCA/maxEigValue <1e-12)

eigvalue_PCA(eigIdx) = []

eigvector_PCA(:,eigIdx) = []

[junk, index] = sort(-eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(index)

eigvector_PCA = eigvector_PCA(:, index)

%=======================================

if options.PCARatio >1

idx = options.PCARatio

if idx <length(eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector_PCA = eigvector_PCA(:,1:idx)

end

elseif options.PCARatio <1

sumEig = sum(eigvalue_PCA)

sumEig = sumEig*options.PCARatio

sumNow = 0

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx)

if sumNow >= sumEig

break

end

end

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector_PCA = eigvector_PCA(:,1:idx)

end

%=======================================

eigvalue_PCA = eigvalue_PCA.^-.5

data = (data*eigvector_PCA).*repmat(eigvalue_PCA',nSmp,1)

else

ddata = data*data'

ddata = max(ddata,ddata')

[eigvector, eigvalue_PCA] = eig(ddata)

eigvalue_PCA = diag(eigvalue_PCA)

clear ddata

maxEigValue = max(eigvalue_PCA)

eigIdx = find(eigvalue_PCA/maxEigValue <1e-12)

eigvalue_PCA(eigIdx) = []

eigvector(:,eigIdx) = []

[junk, index] = sort(-eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(index)

eigvector = eigvector(:, index)

%=======================================

if options.PCARatio >1

idx = options.PCARatio

if idx <length(eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector = eigvector(:,1:idx)

end

elseif options.PCARatio <1

sumEig = sum(eigvalue_PCA)

sumEig = sumEig*options.PCARatio

sumNow = 0

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx)

if sumNow >= sumEig

break

end

end

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector = eigvector(:,1:idx)

end

%=======================================

eigvalue_PCA = eigvalue_PCA.^-.5

eigvector_PCA = (data'*eigvector).*repmat(eigvalue_PCA',nFea,1)

data = eigvector

clear eigvector

end

else

if ~bChol

DPrime = data'*data

% options.ReguAlpha = nSmp*options.ReguAlpha

switch lower(options.ReguType)

case {lower('Ridge')}

for i=1:size(DPrime,1)

DPrime(i,i) = DPrime(i,i) + options.ReguAlpha

end

case {lower('Tensor')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR

case {lower('Custom')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR

otherwise

error('ReguType does not exist!')

end

DPrime = max(DPrime,DPrime')

end

end

[nSmp,nFea] = size(data)

Hb = zeros(nClass,nFea)

for i = 1:nClass,

index = find(gnd==classLabel(i))

classMean = mean(data(index,:),1)

Hb (i,:) = sqrt(length(index))*classMean

end

elapse.timeW = 0

elapse.timePCA = cputime - tmp_T

tmp_T = cputime

if bPCA

[dumpVec,eigvalue,eigvector] = svd(Hb,'econ')

eigvalue = diag(eigvalue)

eigIdx = find(eigvalue <1e-3)

eigvalue(eigIdx) = []

eigvector(:,eigIdx) = []

eigvalue = eigvalue.^2

eigvector = eigvector_PCA*(repmat(eigvalue_PCA,1,length(eigvalue)).*eigvector)

else

WPrime = Hb'*Hb

WPrime = max(WPrime,WPrime')

dimMatrix = size(WPrime,2)

if Dim >dimMatrix

Dim = dimMatrix

end

if isfield(options,'bEigs')

if options.bEigs

bEigs = 1

else

bEigs = 0

end

else

if (dimMatrix >1000 &Dim <dimMatrix/10) | (dimMatrix >500 &Dim <dimMatrix/20) | (dimMatrix >250 &Dim <dimMatrix/30)

bEigs = 1

else

bEigs = 0

end

end

if bEigs

%disp('use eigs to speed up!')

option = struct('disp',0)

if bChol

option.cholB = 1

[eigvector, eigvalue] = eigs(WPrime,R,Dim,'la',option)

else

[eigvector, eigvalue] = eigs(WPrime,DPrime,Dim,'la',option)

end

eigvalue = diag(eigvalue)

else

[eigvector, eigvalue] = eig(WPrime,DPrime)

eigvalue = diag(eigvalue)

[junk, index] = sort(-eigvalue)

eigvalue = eigvalue(index)

eigvector = eigvector(:,index)

if Dim <size(eigvector,2)

eigvector = eigvector(:, 1:Dim)

eigvalue = eigvalue(1:Dim)

end

end

end

for i = 1:size(eigvector,2)

eigvector(:,i) = eigvector(:,i)./norm(eigvector(:,i))

end

elapse.timeMethod = cputime - tmp_T

elapse.timeAll = elapse.timePCA + elapse.timeMethod

二维主要有： 1.基于模板匹配的方法；2.基于奇异值特征方法3.子空虚携间分差燃伏析法；4.局部保持投影段缓(Locality Preserving Projections，LPP) 三维人脸识别方法有： 1.基于图像特征的方法；2.基于模型可变参数的方法。 每种算法都有自己的优缺点，不好说。

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