高分求matlab pca人脸识别程序

function pca (path, trainList, subDim)

%

% PROTOTYPE

% function pca (path, trainList, subDim)

%

% USAGE EXAMPLE(S)

% pca ('C:/FERET_Normalised/', trainList500Imgs, 200)

%

% GENERAL DESCRIPTION

% Implements the standard Turk-Pentland Eigenfaces method. As a final

% result, this function saves pcaProj matrix to the disk with all images

% projected onto the subDim-dimensional subspace found by PCA.

%

% REFERENCES

% M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive

% Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86

%

% M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings

% of the IEEE Conference on Computer Vision and Pattern Recognition,

% 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591

%

%

% INPUTS:

% path - full path to the normalised images from FERET database

% trainList - list of images to be used for training. names should be

% without extension and .pgm will be added automatically

% subDim - Numer of dimensions to be retained (the desired subspace

% dimensionality). if this argument is ommited, maximum

% non-zero dimensions will be retained, i.e. (number of training images) - 1

%

% OUTPUTS:

% Function will generate and save to the disk the following outputs:

% DATA - matrix where each column is one image reshaped into a vector

% - this matrix size is (number of pixels) x (number of images), uint8

% imSpace - same as DATA but only images in the training set

% psi - mean face (of training images)

% zeroMeanSpace - mean face subtracted from each row in imSpace

% pcaEigVals - eigenvalues

% w - lower dimensional PCA subspace

% pcaProj - all images projected onto a subDim-dimensional space

%

% NOTES / COMMENTS

% * The following files must either be in the same path as this function

% or somewhere in Matlab's path:

% 1. listAll.mat - containing the list of all 3816 FERET images

%

% ** Each dimension of the resulting subspace is normalised to unit length

%

% *** Developed using Matlab 7

%

%

% REVISION HISTORY

% -

%

% RELATED FUNCTIONS (SEE ALSO)

% createDistMat, feret

%

% ABOUT

% Created: 03 Sep 2005

% Last Update: -

% Revision: 1.0

%

% AUTHOR: Kresimir Delac

% mailto: kdelac@ieee.org

% URL: http://www.vcl.fer.hr/kdelac

%

% WHEN PUBLISHING A PAPER AS A RESULT OF RESEARCH CONDUCTED BY USING THIS CODE

% OR ANY PART OF IT, MAKE A REFERENCE TO THE FOLLOWING PAPER:

% Delac K., Grgic M., Grgic S., Independent Comparative Study of PCA, ICA, and LDA

% on the FERET Data Set, International Journal of Imaging Systems and Technology,

% Vol. 15, Issue 5, 2006, pp. 252-260

%

% If subDim is not given, n - 1 dimensions are

% retained, where n is the number of training images

if nargin <3

subDim = dim - 1

end

disp(' ')

load listAll

% Constants

numIm = 3816

% Memory allocation for DATA matrix

fprintf('Creating DATA matrix\n')

tmp = imread ( [path char(listAll(1)) '.pgm'] )

[m, n] = size (tmp)% image size - used later also!!!

DATA = uint8 (zeros(m*n, numIm))% Memory allocated

clear str tmp

% Creating DATA matrix

for i = 1 : numIm

im = imread ( [path char(listAll(i)) '.pgm'] )

DATA(:, i) = reshape (im, m*n, 1)

end

save DATA DATA

clear im

% Creating training images space

fprintf('Creating training images space\n')

dim = length (trainList)

imSpace = zeros (m*n, dim)

for i = 1 : dim

index = strmatch (trainList(i), listAll)

imSpace(:, i) = DATA(:, index)

end

save imSpace imSpace

clear DATA

% Calculating mean face from training images

fprintf('Zero mean\n')

psi = mean(double(imSpace'))'

save psi psi

% Zero mean

zeroMeanSpace = zeros(size(imSpace))

for i = 1 : dim

zeroMeanSpace(:, i) = double(imSpace(:, i)) - psi

end

save zeroMeanSpace zeroMeanSpace

clear imSpace

% PCA

fprintf('PCA\n')

L = zeroMeanSpace' * zeroMeanSpace% Turk-Pentland trick (part 1)

[eigVecs, eigVals] = eig(L)

diagonal = diag(eigVals)

[diagonal, index] = sort(diagonal)

index = flipud(index)

pcaEigVals = zeros(size(eigVals))

for i = 1 : size(eigVals, 1)

pcaEigVals(i, i) = eigVals(index(i), index(i))

pcaEigVecs(:, i) = eigVecs(:, index(i))

end

pcaEigVals = diag(pcaEigVals)

pcaEigVals = pcaEigVals / (dim-1)

pcaEigVals = pcaEigVals(1 : subDim)% Retaining only the largest subDim ones

pcaEigVecs = zeroMeanSpace * pcaEigVecs% Turk-Pentland trick (part 2)

save pcaEigVals pcaEigVals

% Normalisation to unit length

fprintf('Normalising\n')

for i = 1 : dim

pcaEigVecs(:, i) = pcaEigVecs(:, i) / norm(pcaEigVecs(:, i))

end

% Dimensionality reduction.

fprintf('Creating lower dimensional subspace\n')

w = pcaEigVecs(:, 1:subDim)

save w w

clear w

% Subtract mean face from all images

load DATA

load psi

zeroMeanDATA = zeros(size(DATA))

for i = 1 : size(DATA, 2)

zeroMeanDATA(:, i) = double(DATA(:, i)) - psi

end

clear psi

clear DATA

% Project all images onto a new lower dimensional subspace (w)

fprintf('Projecting all images onto a new lower dimensional subspace\n')

load w

pcaProj = w' * zeroMeanDATA

clear w

clear zeroMeanDATA

save pcaProj pcaProj

%更多给我邮件 我的空间有邮件地址

function pca (path, trainList, subDim)

%

% PROTOTYPE

% function pca (path, trainList, subDim)

%

% USAGE EXAMPLE(S)

% pca ('C:/FERET_Normalised/', trainList500Imgs, 200)

%

% GENERAL DESCRIPTION

% Implements the standard Turk-Pentland Eigenfaces method. As a final

% result, this function saves pcaProj matrix to the disk with all images

% projected onto the subDim-dimensional subspace found by PCA.

%

% REFERENCES

% M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive

% Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86

%

% M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings

% of the IEEE Conference on Computer Vision and Pattern Recognition,

% 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591

%

%

% INPUTS:

% path - full path to the normalised images from FERET database

% trainList - list of images to be used for training. names should be

% without extension and .pgm will be added automatically

% subDim- Numer of dimensions to be retained (the desired subspace

% dimensionality). if this argument is ommited, maximum

% non-zero dimensions will be retained, i.e. (number of training images) - 1

%

% OUTPUTS:

% Function will generate and save to the disk the following outputs:

% DATA - matrix where each column is one image reshaped into a vector

% - this matrix size is (number of pixels) x (number of images), uint8

% imSpace - same as DATA but only images in the training set

% psi - mean face (of training images)

% zeroMeanSpace - mean face subtracted from each row in imSpace

% pcaEigVals- eigenvalues

% w - lower dimensional PCA subspace

% pcaProj - all images projected onto a subDim-dimensional space

%

% NOTES / COMMENTS

% * The following files must either be in the same path as this function

% or somewhere in Matlab's path:

% 1. listAll.mat - containing the list of all 3816 FERET images

%

% ** Each dimension of the resulting subspace is normalised to unit length

%

% *** Developed using Matlab 7

%

%

% REVISION HISTORY

% -

%

% RELATED FUNCTIONS (SEE ALSO)

% createDistMat, feret

%

% ABOUT

% Created:03 Sep 2005

% Last Update:-

% Revision: 1.0

%

% AUTHOR: Kresimir Delac

% mailto: kdelac@ieee.org

% URL: http://www.vcl.fer.hr/kdelac

%

% WHEN PUBLISHING A PAPER AS A RESULT OF RESEARCH CONDUCTED BY USING THIS CODE

% OR ANY PART OF IT, MAKE A REFERENCE TO THE FOLLOWING PAPER:

% Delac K., Grgic M., Grgic S., Independent Comparative Study of PCA, ICA, and LDA

% on the FERET Data Set, International Journal of Imaging Systems and Technology,

% Vol. 15, Issue 5, 2006, pp. 252-260

%

% If subDim is not given, n - 1 dimensions are

% retained, where n is the number of training images

if nargin <3

subDim = dim - 1

end

disp(' ')

load listAll

% Constants

numIm = 3816

% Memory allocation for DATA matrix

fprintf('Creating DATA matrix\n')

tmp = imread ( [path char(listAll(1)) '.pgm'] )

[m, n] = size (tmp) % image size - used later also!!!

DATA = uint8 (zeros(m*n, numIm)) % Memory allocated

clear str tmp

% Creating DATA matrix

for i = 1 : numIm

im = imread ( [path char(listAll(i)) '.pgm'] )

DATA(:, i) = reshape (im, m*n, 1)

end

save DATA DATA

clear im

% Creating training images space

fprintf('Creating training images space\n')

dim = length (trainList)

imSpace = zeros (m*n, dim)

for i = 1 : dim

index = strmatch (trainList(i), listAll)

imSpace(:, i) = DATA(:, index)

end

save imSpace imSpace

clear DATA

% Calculating mean face from training images

fprintf('Zero mean\n')

psi = mean(double(imSpace'))'

save psi psi

% Zero mean

zeroMeanSpace = zeros(size(imSpace))

for i = 1 : dim

zeroMeanSpace(:, i) = double(imSpace(:, i)) - psi

end

save zeroMeanSpace zeroMeanSpace

clear imSpace

% PCA

fprintf('PCA\n')

L = zeroMeanSpace' * zeroMeanSpace% Turk-Pentland trick (part 1)

[eigVecs, eigVals] = eig(L)

diagonal = diag(eigVals)

[diagonal, index] = sort(diagonal)

index = flipud(index)

pcaEigVals = zeros(size(eigVals))

for i = 1 : size(eigVals, 1)

pcaEigVals(i, i) = eigVals(index(i), index(i))

pcaEigVecs(:, i) = eigVecs(:, index(i))

end

pcaEigVals = diag(pcaEigVals)

pcaEigVals = pcaEigVals / (dim-1)

pcaEigVals = pcaEigVals(1 : subDim) % Retaining only the largest subDim ones

pcaEigVecs = zeroMeanSpace * pcaEigVecs % Turk-Pentland trick (part 2)

save pcaEigVals pcaEigVals

% Normalisation to unit length

fprintf('Normalising\n')

for i = 1 : dim

pcaEigVecs(:, i) = pcaEigVecs(:, i) / norm(pcaEigVecs(:, i))

end

% Dimensionality reduction.

fprintf('Creating lower dimensional subspace\n')

w = pcaEigVecs(:, 1:subDim)

save w w

clear w

% Subtract mean face from all images

load DATA

load psi

zeroMeanDATA = zeros(size(DATA))

for i = 1 : size(DATA, 2)

zeroMeanDATA(:, i) = double(DATA(:, i)) - psi

end

clear psi

clear DATA

% Project all images onto a new lower dimensional subspace (w)

fprintf('Projecting all images onto a new lower dimensional subspace\n')

load w

pcaProj = w' * zeroMeanDATA

clear w

clear zeroMeanDATA

save pcaProj pcaProj

基于特征脸(PCA)的人脸识别方法

特征脸方法是基于KL变换的人脸识别方法，KL变换是图像压缩的一种最优正交变换。高维的图像空间经过KL变换后得到一组新的正交基，保留其中重要的正交基，由这些基可以张成低维线性空间。如果假设人脸在这些低维线性空间的投影具有可分性，就可以将这些投影用作识别的特征矢量，这就是特征脸方法的基本思想。这些方法需要较多的训练样本，而且完全是基于图像灰度的统计特性的。目前有一些改进型的特征脸方法。

    比如人脸灰度照片40x40=1600个像素点，用每个像素的灰度值组成的矩阵代表这个人的人脸。那么这个人人脸就要1600 个特征。拿一堆这样的样本过来做pca，抽取得到的只是在统计意义下能代表某个样本的几个特征。

   人脸识别可以采用神经网 络深度学习的思路，国内的ColorReco在这边有比较多的案例。

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