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?

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Balaji M. Sontakke 2018-6-19

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https://ww2.mathworks.cn/matlabcentral/answers/406341-sir-am-having-the-four-mat-files-which-of-two-contains-features-from-minutie-and-other-two-contain

编辑： Andrei Bobrov 2018-6-22

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?

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Andrei Bobrov 2018-6-22

How combine?

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Answer 1

Andrei Bobrov 2018-6-19

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编辑：Andrei Bobrov 2018-6-22

在 MATLAB Online 中打开

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);

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Balaji M. Sontakke 2018-6-20

编辑：Andrei Bobrov 2018-6-22

在 MATLAB Online 中打开

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