PCA codes input, use for PalmPrint Recognition - matlab

Question

I'm new to Matlab.
I'm trying to apply PCA function(URL listed below)into my palm print recognition program to generate the eigenpalms. My palm print grey scale images dimension are 450*400.
Before using it, I was trying to study these codes and add some codes to save the eigenvector as .mat file. Some of the %comments added by me for my self understanding.
After a few days of studying, I still unable to get the answers.
I decided to ask for helps.I have a few questions to ask regarding this PCA.m.
PCA.m
What is the input of the "options" should be? of "PCA(data,details,options)"
(is it an integer for reduced dimension? I was trying to figure out where is the "options" value passing, but still unable to get the ans. The msgbox of "h & h2", is to check the codes run until where. I was trying to use integer of 10, but the PCA.m processed dimension are 400*400.)
The "eigvector" that I save as ".mat" file is ready to perform Euclidean distance classifier with other eigenvector? (I'm thinking that eigvector is equal to eigenpalm, like in face recognition, the eigen faces. I was trying to convert the eigenvector matrix back to image, but the image after PCA process is in Black and many dots on it)
mySVD.m
In this function, there are two values that can be changed, which are MAX_MATRIX_SIZE set by 1600 and EIGVECTOR_RATIO set by 0.1%. May I know these values will affect the results? ( I was trying to play around with the values, but I cant see the different. My palm print image dimension is set by 450*400, so the Max_matrix_size should set at 180,000?)
** I hope you guys able to understand what I'm asking, please help, Thanks guys (=
Original Version : http://www.cad.zju.edu.cn/home/dengcai/Data/code/PCA.m
mySVD: http://www.cad.zju.edu.cn/home/dengcai/Data/code/mySVD.m
% Edited Version by me
function [eigvector, eigvalue] = PCA(data,details,options)
%PCA Principal Component Analysis
%
% Usage:
% [eigvector, eigvalue] = PCA(data, options)
% [eigvector, eigvalue] = PCA(data)
%
% Input:
% data - Data matrix. Each row vector of fea is a data point.
% fea = finite element analysis ?????
% options.ReducedDim - The dimensionality of the reduced subspace. If 0,
% all the dimensions will be kept.
% Default is 0.
%
% 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 PCA eigen-problem.
%
% Examples:
% fea = rand(7,10);
% options=[]; %store an empty matrix in options
% options.ReducedDim=4;
% [eigvector,eigvalue] = PCA(fea,4);
% Y = fea*eigvector;
%
% version 3.0 --Dec/2011
% version 2.2 --Feb/2009
% version 2.1 --June/2007
% version 2.0 --May/2007
% version 1.1 --Feb/2006
% version 1.0 --April/2004
%
% Written by Deng Cai (dengcai AT gmail.com)
%
if (~exist('options','var'))
%A = exist('name','kind')
% var = Checks only for variables.
%http://www.mathworks.com/help/matlab/matlab_prog/symbol-reference.html#bsv2dx9-1
%The tilde "~" character is used in comparing arrays for unequal values,
%finding the logical NOT of an array,
%and as a placeholder for an input or output argument you want to omit from a function call.
options = [];
end
h2 = msgbox('not yet');
ReducedDim = 0;
if isfield(options,'ReducedDim')
%tf = isfield(S, 'fieldname')
h2 = msgbox('checked');
ReducedDim = options.ReducedDim;
end
[nSmp,nFea] = size(data);
if (ReducedDim > nFea) || (ReducedDim <=0)
ReducedDim = nFea;
end
if issparse(data)
data = full(data);
end
sampleMean = mean(data,1);
data = (data - repmat(sampleMean,nSmp,1));
[eigvector, eigvalue] = mySVD(data',ReducedDim);
eigvalue = full(diag(eigvalue)).^2;
if isfield(options,'PCARatio')
sumEig = sum(eigvalue);
sumEig = sumEig*options.PCARatio;
sumNow = 0;
for idx = 1:length(eigvalue)
sumNow = sumNow + eigvalue(idx);
if sumNow >= sumEig
break;
end
end
eigvector = eigvector(:,1:idx);
end
%dt get from C# program, user ID and name
evFolder = 'ev\';
userIDName = details; %get ID and Name
userIDNameWE = strcat(userIDName,'\');%get ID and Name with extension
filePath = fullfile('C:\Users\***\Desktop\Data Collection\');
userIDNameFolder = strcat(filePath,userIDNameWE); %ID and Name folder
userIDNameEVFolder = strcat(userIDNameFolder,evFolder);%EV folder in ID and Name Folder
userIDNameEVFile = strcat(userIDNameEVFolder,userIDName); % EV file with ID and Name
if ~exist(userIDNameEVFolder, 'dir')
mkdir(userIDNameEVFolder);
end
newFile = strcat(userIDNameEVFile,'_1');
searchMat = strcat(newFile,'.mat');
if exist(searchMat, 'file')
filePattern = strcat(userIDNameEVFile,'_');
D = dir([userIDNameEVFolder, '*.mat']);
Num = length(D(not([D.isdir])))
Num=Num+1;
fileName = [filePattern,num2str(Num)];
save(fileName,'eigvector');
else
newFile = strcat(userIDNameEVFile,'_1');
save(newFile,'eigvector');
end

Answer 1

You pass options in a structure, for instance:
options.ReducedDim = 2;
or
options.PCARatio =0.4;
The option ReducedDim selects the number of dimensions you want to use to represent the final projection of the original matrix. For instance if you pick option.ReducedDim = 2 you use only the two eigenvectors with largest eigenvalues (the two principal components) to represent your data (in effect the PCA will return the two eigenvectors with largest eigenvalues).
PCARatio instead allows you to pick the number of dimensions as the first eigenvectors with largest eigenvalues that account for fraction PCARatio of the total sum of eigenvalues.
In mySVD.m, I would not increase the default values unless you expect more than 1600 eigenvectors to be necessary to describe your dataset. I think you can safely leave the default values.