FP_IITB.pdf

7/27/2019 FP_IITB.pdf

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Fingerprint Matching

Overview of fingerprint Matching Process

Registration

Get person As fingerprint Image

Extract Features and store against A is ID

Verification

Get A fingerprint Image

Extract Featuresretrieve features stored in database for A

Match both features

(1 : 1] matching

IdentificationGot some fingerprint image (who is this?)

Extract features

Match against every feature stored in the database

Get the ID for the best matching features, he/she is the likely person

(1 to N matching)


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The Fingerprint ImageScanners provides API to get the image

Typical fingerprint has 256 gray scales, so needs 1

byte to represent each pixel.

value 0 is BLACK and value 255 is WHITE

Typical resolution is 500 PPI

Simple representation of an Image would be

unsigned char image [Height] [Width];

Getting a pixel value in an Image is indexing

unsigned char value = image[row] [column];


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The FeaturesFingerprint have ridges and valleys

(the black are the ridges and white

valleys)

Ridge ending and Ridge Bifurcation

are of interest.

Find these ridge endings and

bifurcations and store their position

X,Y and angleThese features are minutiae and the

set of all the minutiae together is the

template!! (with some additional info)

bifurcations

ending


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Feature MatchingGiven two sets of features determine if they are of the same finger.

Find the amount of similarity between features, if it is above some threshold then

probably they are from the same finger.

Point pattern Matching Problem.


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Steps in feature extraction-Reduce Noise from Image

Histogram Equalization, Gaussian Filter

-Segment Image (identify the background and foreground)Simple variance based to complex direction based

-Binarize Image

Simple Threshold or adaptive threshold or direction based

-Image Thinning

reduce to 1 pixel width without breaking lines.-Minutiae Detection

Find pixel where ridge ends or bifurcates

-Minutiae Post Processing

Remove false minutiae such as border, artifacts generated by

earlier processing, and not so sure!!-Store minutiae in database (along with persons ID)



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Noise Reduction


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Noise ReductionConsider Image represented as 2 dimensional array of unsigned char with h as height

and w as width. Following code filters the image and creates a new filtered image

using Gaussian 3x3 kernel

unsigned char gfilt[3][3] = { { 1, 2, 1 },

{ 2, 4, 2 },

{ 1, 2, 1 } };

for ( int a = 1; a < height - 1; a++ ) {for ( int b = 1; b < width1; b++ ) {

value = 0;

for ( int j = -1; j


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Binarization

Adaptive Directional Threshold


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Thinning


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Minutiae Detection


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Minutiae DetectionCompute Crossing number for a black pixel in image.

Crossing at P is define as CN(P) = half the sum of the differences between the

adjacentpixels in the 8 neighborhood of P

if CN = 1 then termination if CN = 3 then bifurcation above 3 is complex minutiae

such as cross over etc.

CN(P) = j = 0..8 | val (P j mod 8)val (p j-1)|

Here P j are pixels belonging to the 8 neighborhood of P, for example if the current

pixel we are considering is at row = 10 and column = 10 then we sum the pixel

values at ABS(P(10,9)P(9,9)), ABS((P(11,9)P(10,9)), ABS((P(11,10)P(11,9)) and so

on.

Check this out by actually drawing a 8X8 neighborhood!!!


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Fingerprint Matching// MINUTIAE COMPUTATION ON THINNED IMAGE

#define PVAL(x) (x) == 0 ? 1 : 0

int FPDetectMinutiae ( unsigned char *image, int h, int w, char *minfile)

{

int r, c, count, cn;

FILE *fp;if (( fp = fopen(minfile,"w")) == NULL )return -1;

for ( r = 16; r < h - 16; r++ ) {

for ( c = 16; c < w - 16; c++ ) {

if ( image[r*w+c] == WHITE_PIXEL ) continue;

cn = 0;

cn += abs(PVAL(image[(r-1)*w+c]) - PVAL(image[(r-1)*w+(c-1)]));

cn += abs(PVAL(image[(r-1)*w+(c+1)]) - PVAL(image[(r-1)*w+c]));

cn += abs(PVAL(image[(r)*w+(c+1)]) - PVAL(image[(r-1)*w+(c+1)]));

cn += abs(PVAL(image[(r+1)*w+(c+1)]) - PVAL(image[(r)*w+(c+1)]));cn += abs(PVAL(image[(r+1)*w+c]) - PVAL(image[(r+1)*w+(c+1)]));

cn += abs(PVAL(image[(r+1)*w+(c-1)]) - PVAL(image[(r+1)*w+c]));

cn += abs(PVAL(image[(r)*w+(c-1)]) - PVAL(image[(r+1)*w+(c-1)]));

cn += abs(PVAL(image[(r-1)*w+(c-1)]) - PVAL(image[r*w+(c-1)]));

cn /= 2;

// Termination Minutia

if ( cn == 1 )

fprintf(fp,"%d %d 0 0\n", c, r );

// Bifurcation or complex minutiaelse if ( cn >= 3 )

fprintf(fp,"%d %d 1 0\n", c, r );

}

}

fclose(fp);

return 0;

}

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