Fingerprint Recognition Project- Implementation Code

* Project Name:   Fingerprint Recognition Project- Implementation Code
* Programmer:   msi_333
* Type:   Security
* Technology:  Matlab
* IDE:   Matlab
 Description:   Includes steps such as : Binarization, Normalization, Segmentation, Principal component analysis (PCA) , remove Noise, reduce Resolution
Code.

Code sample :
matlab code

function menutiaeTable=Finger_Print(originalImg)


img=imread(originalImg);

segImage=Segmentation(img);
normImage=Normalization(segImage);
enhancedImg=removeNoise(normImage);
binaryImg=binnary(enhancedImg,17 ,2.24 , 0.85 );%Binarization(enhancedImg);
thinningImg=bwmorph(1-binaryImg,'thin',Inf);
[menutiaeImg, menutiaeTable]=menutieaExtraction(thinningImg);


figure
imshow(enhancedImg),title('Image After Remove Noise');
figure
imshow(binaryImg),title('Image After Binarization');
figure
imshow(1-thinningImg),title('Image After Thinning(Skelton)');
figure;
imshow(menutiaeImg),title('Image Ridges White(ending)-Blue(Bifiraction)');

lable='Image After Remove Noise and Ridges';
margeImg(enhancedImg, menutiaeTable, lable)

lable='Image After Binarization and Ridges';
margeImg( binaryImg, menutiaeTable, lable)

lable='Thinning(Skelton) Image and Ridges';
margeImg( 1-thinningImg, menutiaeTable, lable)

Reduce Image resolution
matlab code

function reducedResImg=reduceReselution(img)

[W,H]=size(img) ;

for i=1:1:floor(W/2) ;
    for j=1:1:floor(H/2) ;
        reducedResImg(i,j)=img(i*2,j*2) ;
    end 
end

Binarization:
matlab code

function binary=Binarization(img)

globalThresh=graythresh(img);

[H W]=size(img);

for i=1:H
    for j=1:W
     
        if(img(i,j)>=globalThresh)
            binary(i,j)=1;
        else
            binary(i,j)=0; 
        end
    end
end

Attachments

FingerPrint project.zip

Finger Print Classification

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