pca.rar_PCA重建_pca_pca 图像重建_pca人脸重建_人脸重建

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://222.178.203.72:19005/whst/63/_vvvzubkzedqzgq//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;