minietua algorithm

7/31/2019 Minietua Algorithm

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Minietua Algorithm


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What is Minietua Matching Algorithm?

It is algorithm to extracting minietua from theFingerprints and match them to show if bothfingerprint belong to same person or not.


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Fingerprint Description

The lines that flow in various pattern acrossFingerprint are called ridges and spaces betweenridges are called valley.

These ridges are compared between one fingerprintand other when matching.

Comparison of all ridges is a lengthy task. Henceinstead of ridges minietua are matched.


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Ridge


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What actually is minietua?

Minutiae are the special points of interest in aFingerprint such as ridge ending or ridge bifurcation.

Ridge Ending RidgeBifurcation/Intersection


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Types of minietua

Ridge Ending.

Ridge Intersection.

Ridge Bifurcation.

Lake


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Steps in Minietua Matching

Step 1 : Binarization.

Step 2 : Smoothening.

Step 3 : Thinning (Skeletonization).

Step 4 : Extract Minutiae.Step 5 : Match Minutiae from Database.


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Binarization

Binarization is the process to convert a gray scale imageinto a Binary Image to improve the contrast betweenridges and valleys in the fingerprint image.

How to Binarize image.

Calculate Gray level of each pixel by taking mean of RGB values atthose pixel.

Calculate the Global mean by taking mean of all above calculatedvalues.

Calculate the local mean of small regions of the image.

If the local mean is greater than the global mean then local greymean is calculated by formula localmean = 0.75*global mean + 0.25*localmean

Now each pixel is compared with local mean if its value is greaterthan local mean then it is set to binary one else it is set to binaryzero.


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Original FingerprintImage

Binary FingerprintImage


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Smoothen Image

Image is smoothened by processing the image withthe filter.

Here, Mean Filter is used with properties

filter ={0.0625 0.1250 0.06250.1250 0.2500 0.12500.0625 0.1250 0.0625 }

Every pixel is calculated for weight of its neighbour

by adding corresponding value of binary pixel.

If weight is greater than 0.5 than change the pixelvalue to binary one.


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BinaryFingerprint

SmoothenFingerprint


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Skeletonization (Thinning)

Sekletonization is done through Zhang-Suen algorithm on binary image from laststep.

This algorithm is made by two subiterations. In the first one, a pixel I(i,j) isdeleted if the following condition aresatisfied: It has at least two black neighbors and not more

than six.

At least one of I(i,j+1), I(i-1,j), and I(i,j-1) are white.

At least one of I(i-1,j), I(i+1,j), and I(i,j-1) are white.


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In the second Iteration. It has at least two black neighbors and not more than

six.

At least one of I(i-1,j), I(i,j+1), and I(i+1,j) are white. At least one of I(i,j+1), I(i+1,j), and I(i,j-1) are white.


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Minietua Extraction

To extract minietua we check for neighborof each pixel

For end points


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For Bifurcation points


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Minietua Matching

The various minietua from last step to theminietua stored in the database arematched and checked for the matched

percentage if more than 70% of theseminietua are matched than fingerprint ismatched.

minietua algorithm

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