pca.m

关于PCA方法的人脸识别的MATLAB算法

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: [e-mail] 
% 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;