Sir, am having the four mat files, which of two contains features from minutie and other two contains feature from invarient moment, can i make fusion of those features by combining it then how?

Sir, am having the four mat files, which of two contains features from minutiae and other two contains feature from invariant moment, can i make fusion of those features by combining it then how?

[Andrei Bobrov]

How combine?

Answer 1

[Andrei Bobrov]

V = {load('db1.mat'),load('db2.mat'),load('db3.mat'),load('db4.mat')};

n = cellfun(@fieldnames,V,'un',0);
V1 = cellfun(@(x,y)[x.(y)],V,[n{:}],'un',0);
V2 = cell2mat(reshape(cat(1,V1{:}]),1200,[]));
V3 = mat2cell(V2,4*ones(size(V2,1)/4,1),7);
out = mat2cell(V3,[400,800],1);

[Balaji M. Sontakke]

sir, I did classification by loading first two mat files (see following code), In your code out is a cell array, how i get reduced_testdata and reduced_traindata for further processing.

 clear all; 
 clc; 

 tic;                  %%calculating elapsed time for execution

 %load mat files

 load('db3.mat');
 load('db4.mat');

 %%reshape into row vector

 reduced_testdata = reshape(reduced_testdata,1,4,100);        
 reduced_traindata = reshape(reduced_traindata,1,4,200);       

 %%adjust dimension
 % Adjust matrix dimension

 P_test = cell2mat(reduced_testdata);                                          % Convert cell array to matrix

 P_train = cell2mat(reduced_traindata); 

 %%rearranges the dimensions of P_test and P_train 

 C = permute(P_test,[1 3 2]);
 P_test = reshape(C,[],size(P_test,2),1);

 C = permute(P_train,[1 3 2]);
 P_train = reshape(C,[],size(P_train,2),1);

 %%labeling class

train_label=load('train_label.txt');  
test_label=load('test_label.txt');   

 %%Normalisation by Z - Scores 

 P_train = zscore(P_train,0,2);
 P_test  =zscore(P_test,0,2); 

 %%classfication

 predictlabel = knnclassify(P_test, P_train,   train_label,3,'euclidean','nearest');
 cp = classperf(test_label,predictlabel);

 Conf_Mat = confusionmat(test_label,predictlabel);
 disp(Conf_Mat);

 %%Evaluate Performance

 [FPR, TPR,Thr, AUC, OPTROCPT]  = perfcurve(predictlabel, test_label,1);

 figure,

 plot(TPR,FPR,'r-','LineWidth',4);
 xlabel('False positive rate')
 ylabel('True positive rate') 
 title('ROC Curve for Classification ')
 Tbl = table(FPR, TPR,Thr)

 [FPR, FNR, Thr]  = perfcurve(predictlabel, test_label,1,'xCrit','fall','xCrit','miss');
Thr = Thr/10;

 figure,

 plot(Thr,FPR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
 xlabel('Threshold')
 ylabel('False positive rate / FAR') 
 title('False Acceptance Rate / FAR ')

 figure,

 plot(Thr,FNR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
 xlabel('Threshold')
 ylabel('False Negative rate / FRR') 
 title('False Rejection Rate / FRR ')
 Tbl = table(FPR, FNR,Thr) 

 fprintf('\n\n Overall accuracy:%f%%\n',cp.CorrectRate*100);

 %%calculating elapsed time for execution
 toc

[Balaji M. Sontakke]

What happen sir not answered?

[Andrei Bobrov]

I don't understand your question:

"..can i make fusion of those features..?"