Fingerprint Recognition Technique(PDF)

FINGERPRINT RECOGNITION

Project ID: 1044

A Final Project Report Submitted to

Biju Patnaik University of Technology, Rourkela

In partial fulfilment of the requirement for the B.Tech Degree

Submitted By

SAILENDRA SAGAR PATRA

SANDEEP KUMAR PANDA

May - 2013

Under the guidance of Mrs. T. Mita Kumari

APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT Pahala, Bhubaneswar, Odisha – 752101, India


A Final Project Report Submitted to

Biju Patnaik University of Technology, Rourkela

In partial fulfilment of the requirement for the B.Tech Degree Submitted By

SAILENDRA SAGAR PATRA Regn. No.-0901314147

SANDEEP KUMAR PANDA Regn. No.-0901314126

May - 2013

Under the guidance of

Mrs. T. Mita Kumari

APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT Pahala, Bhubaneswar, Odisha – 752101, India

APEX INSTITUTE OF TECHNOLOGY & MANAGEMENT

Pahala, Bhubaneswar, Odisha – 752101, India

CERTIFICATE

This is to certify that the project work entitled ‘Fingerprint Recognition’

is a bonafide work done by Sailendra Sagar Patra bearing Registration No.

0901314147 of ECE branch and Sandeep Kumar Panda bearing Registration

No. 0901314126 of ECE branch.

This report is submitted in partial fulfilment of the requirements for the

award of the B.Tech degree under Biju Patnaik University of Technology,

Rourkela, during the year 2012-13.

(Mrs T.Mita Kumari) (Dr. Satya Ranjan Pattanaik)

Project Guide B.Tech Project Coordinator

(Prof. R.C. Das)

PRINCIPAL

Institute Seal


ii

ABSTRACT

The study and implementation of a fingerprint recognition system based on Minutiae based

matching quite frequently used in various fingerprint algorithms and techniques. Human

fingerprints are rich in details called minutiae, which can be used as identification marks for

fingerprint verification. The goal of this project is to develop a complete system for

fingerprint verification through extracting and matching minutiae. To achieve good minutiae

extraction in fingerprints with varying quality, pre-processing in form of image enhancement,

image Binarization and image segmentation is first applied on fingerprints before they are

evaluated. Histogram equalization and Fourier Transform have been used for image

enhancement. Then the fingerprint image is binarized using the local adaptive threshold

method .Minutia Extraction is done by thinning and minutia marking technique. A simple

algorithm technique is used for minutia matching. By using match score method we

differentiate the two fingerprints are same or not.


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ACKNOWLEDGEMENT

It is our proud privilege to epitomize our deepest sense of gratitude and indebtedness

to our guide, Mrs. T.Mita Kumari, for her valuable instructions, guidance and support

throughout our project work. Her inspiring assistance and affectionate care enabled us to

complete our work smoothly and successfully.

We again owe our special thanks to Dr. Satya Ranjan Pattanaik, B.Tech Project

Coordinator for giving us an opportunity to do this project.

We would also like to thank Prof. R.C. Das, Principal, AITM, and Bhubaneswar for

his persistent drive for better quality in everything that happens at AITM. This report is a

dedicated contribution towards that greater goal.

Sailendra Sagar Patra

(Regn. No.-0901314147)

Sandeep Kumar Panda

(Regn. No.-0901314126)


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TABLE OF CONTENTS

ABSTRACT......................................................................................................................... ii

ACKNOWLEDGEMENT ................................................................................................. iii

TABLE OF CONTENTS ................................................................................................... iv

LIST OF FIGURES ........................................................................................................... vi

LIST OF ABBREVIATIONS ........................................................................................... vii

1. Introduction ..................................................................................................................... 1

1.1 Introduction ................................................................................................................. 1

1.2 What is a fingerprint? .................................................................................................. 1

1.3 What is Fingerprint Recognition? ................................................................................ 2

2. Algorithm Level Design and Enhancement Technique ................................................. 4

2.1 Algorithm Level Design .............................................................................................. 4

FLOW CHART ................................................................................................................. 6

2.2 Fingerprint Image Enhancement Technique ................................................................. 7

2.2.1 Histogram Equalization ........................................................................................ 7

2.2.2 Fingerprint Enhancement by Fourier transform ..................................................... 8

2.2.3 Fingerprint Image Binarization ............................................................................. 9

2.3 Fingerprint Image Segmentation ................................................................................ 10

2.3.1 Block direction estimation .................................................................................. 10

2.3.2 ROI extraction by Morphological operations....................................................... 11

2.4.1 Minutia Extraction .............................................................................................. 12

2.4.2 Minutia Marking ................................................................................................. 13

2.4.3 False Minutiae Removal ..................................................................................... 14

2.4.4 Unify Terminations and Bifurcation .................................................................... 16

2.5 Minutia Matching ...................................................................................................... 17

2.5.1 Alignment Stage ................................................................................................. 17

2.5.2 Match Stage ........................................................................................................ 18

3. Results and Discussions ................................................................................................. 20

3.1 Results for Minutiae Extraction algorithm ................................................................. 20

3.2 Comparison Results For Minutiae matching .............................................................. 25

3.2.1 Two Different Fingerprints ................................................................................. 25

3.2.2 Two Fingerprint of a same person with a Little Difference ................................. 26


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4. Conclusion ..................................................................................................................... 27

References.......................................................................................................................... 28


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LIST OF FIGURES Figure 1.1: Fingerprint Image ................................................................................................ 1 Figure 1.2: Two Minutia Features ......................................................................................... 2 Figure 1.3: Verification vs. Identification .............................................................................. 3 Figure 2.1: Simplified Fingerprint Recognition ..................................................................... 4 Figure 2.2: Minutia Extractor ................................................................................................ 4 Figure 2.3: Minutia Matcher ................................................................................................. 5 Figure 2.4: Steps Involved In Fingerprint Recognition .......................................................... 6 Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image ..................... 7 Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization .................................. 8 Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT ............................................ 9 Figure 2.8: (a) Enhanced image, (b) Image after Binarization .............................................. 10 Figure 2.9: (a) Binarization image, (b) Direction Map ......................................................... 11 Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound 12 Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch ........................... 13 Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction .................................. 14 Figure 2.13: False Minutia Points ........................................................................................ 15 Figure 2.14: A Bifurcation to Three Terminations ............................................................... 16 Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization .............................. 20 Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT .................................. 21 Figure 3.3: (a) FFT Image, (b) Image after Binarization ...................................................... 21 Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image ...................... 22 Figure 3.5: (a) Binarization Image, (b) ROI Image .............................................................. 22 Figure 3.6: (a) Open Operation, (b) Close Operation ........................................................... 23 Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image .......................................... 23 Figure 3.8: (a) ROI image, (b) Thinned Image .................................................................... 24 Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning .................................. 24 Figure 3.10: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae Extraction of Second Fingerprint ................................................. 25 Figure 3.11: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae extraction of Second Fingerprint .................................................. 26


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LIST OF ABBREVIATIONS

1.AFRS (Automatic Fingerprint Recognition System)………………………………………2

2.FFT (First Fourier Transform)……………………………………………………………...8

3.ROI (Region Of Interest)…………………………………………………………………..10

4.CN (Crossing Number)…………………………………………………………………….13

5. FVC2000 (Fingerprint Verification competition 2000)…………………………………..20

6.IEEE(Institute Of Electrical Electronics Engineering)…………………………………….28


1

1. Introduction

1.1 Introduction

Fingerprint recognition or fingerprint authentication refers to the automated method of

verifying a match between two human fingerprints [1]. Fingerprints are one of many forms

of biometrics used to identify an individual and verify their identity. Because of their

uniqueness and consistency over time, fingerprints have been used for over a century, more

recently becoming automated (i.e. a biometric) due to advancement in computing

capabilities.

1.2 What is a fingerprint?

Skin on human fingertips contains ridges and valleys which together forms distinctive

patterns. These patterns are fully developed under pregnancy and are permanent

throughout whole lifetime. Prints of those patterns are called fingerprints. Injuries like

cuts, burns and bruises can temporarily damage quality of fingerprints but when fully

healed, patterns will be restored. Through various studies it has been observed that no two

persons have the same fingerprints, hence they are unique for every individual.

Figure 1.1: Fingerprint Image

Chapter-1


2

However, shown by intensive research on fingerprint recognition, fingerprints are not

distinguished by their ridges and furrows, but by features called Minutia, which are some

abnormal points on the ridges (Figure 1.2)

Among the variety of minutia types reported in literatures, two are mostly significant and

in heavy usage:

Ridge ending- the abrupt end of a ridge

Ridge bifurcation- a single ridge that divides into two ridges

Figure 1.2: Two Minutia Features

1.3 What is Fingerprint Recognition?

Fingerprint recognition (sometimes referred to as dactyloscopy) is the process of

comparing questioned and known fingerprint against another fingerprint to determine if the

impressions are from the same finger or palm. It includes two sub-domains: one is fingerprint

verification and the other is fingerprint identification (Figure 1.3). In addition, different from

the manual approach for fingerprint recognition by experts, the fingerprint recognition here is

referred as AFRS (Automatic Fingerprint Recognition System) [2,3], which is program-

based.


3

However, in all fingerprint recognition problems, either verification(one to one

matching) or identification(one to many matching), the underlining principles of well defined

representation of a fingerprint and matching remains the same.

Figure 1.3: Verification vs. Identification


4

2. Algorithm Level Design and Enhancement Technique

2.1 Algorithm Level Design

A fingerprint recognition system constitutes of fingerprint acquiring device, minutia

extractor and minutia matcher

Figure 2.1: Simplified Fingerprint Recognition

For fingerprint acquisition, optical or semi-conduct sensors are widely used [3]. They

have high efficiency and acceptable accuracy except for some cases that the user’s finger is

too dirty or dry.

The minutia extractor and matcher modules have been explained in detail in the next

part for algorithm design and other subsequent sections. To implement a minutia extractor, a

three-stage approach is widely used by Researchers. They are pre processing, minutia

extraction and post processing stage.

Figure 2.2: Minutia Extractor

Chapter- 2


5

For the fingerprint image pre processing stage, Histogram Equalization and Fourier

Transform have been used to do image enhancement. And then the fingerprint image is

binarized using the locally adaptive threshold method. The image segmentation task is

fulfilled by a three-step approach: block direction estimation, segmentation by direction

intensity and Region of Interest extraction by Morphological operations. For minutia

extraction stage, iterative parallel thinning algorithm is used. The minutia marking is a

relatively simple task. For the post processing stage, a more rigorous algorithm is developed

to remove false minutia. Also a novel representation for bifurcations is proposed to unify

terminations and bifurcations.

Figure 2.3: Minutia Matcher

The minutia matcher chooses any two minutiae as a reference minutia pair and then

matches their associated ridges first. If the ridges match well, the two fingerprint images are

aligned and matching is conducted for all remaining minutia.


6

FLOW CHART

Figure 2.4: Steps Involved In Fingerprint Recognition

LOAD IMAGE

HISTOGRAM EQUALIZATION

ENHANCEMENT USING FFT

BINARIZATION

RIDGE DIRECTION

THINING

MINUTIA MARKING

TEMPLATE

ALLIGN AND MATCH TEMPLATE

ROI

IMAGE AQUAISATION

PRE PROCESSING STAGE

MINUTIAE EXTRACTION

MINUTIAE MATCH


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2.2 Fingerprint Image Enhancement Technique

The first step in the minutiae extraction stage is Fingerprint Image enhancement. This is

mainly done to improve the image quality and to make it clearer for further operations. Often

fingerprint images from various sources lack sufficient contrast and clarity. Hence image

enhancement is necessary and a major challenge in all fingerprint techniques to improve the

accuracy of matching. It increases the contrast between ridges and furrows and connects the

some of the false broken points of ridges due to insufficient amount of ink or poor quality of

sensor input.

In our project we have implemented three techniques: Histogram Equalization, Fast

Fourier Transformation and Image Binarization.

2.2.1 Histogram Equalization Histogram equalization is a technique of improving the global contrast of an image by

adjusting the intensity distribution on a histogram [4]. This allows areas of lower local

contrast to gain a higher contrast without affecting the global contrast. Histogram

equalization accomplishes this by effectively spreading out the most frequent intensity

values.

The original histogram of a fingerprint image has the bimodal type(Figure 2.5(a),

the histogram after the histogram equalization occupies all the range from 0 to 255 and the

visualization effect is enhanced(Figure 2.5(b)).

(a) (b)

Figure 2.5: (a) Histogram of an image, (b) Histogram equalization of an image


8

(a) (b)

Figure 2.6: (a) Original image, (b) Enhanced Image after Equalization

2.2.2 Fingerprint Enhancement by Fourier transform The image was divided into small processing blocks (32 by 32 pixels) and the Fourier

transform was performed according to:

Nvy

MuxjyxfvuF 2exp),(),( (2.1)

for u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31.

In order to enhance a specific block by its dominant frequencies, the FFT of the block

was multiplied by its magnitude a set of times [4] .Where the magnitude of the original FFT

= abs (F(u ,v)) = |F(u ,v)|.

The enhanced block is obtained according to:

kvuFvuFFyxg ),(,),( 1 (2.2)

Where F-1(F(u,v)) is done by:

Nvy

MuxjvuF

MNyxf

M

x

N

y2exp,1),(

1

0

1

0 (2.3)

for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.


9

The k in formula (2) is an experimentally determined constant, which was k=0.45 to

calculate.

While having a higher "k" improves the appearance of the ridges, filling up small

holes in ridges, having too high a "k" can result in false joining of ridges. Thus a termination

might become a bifurcation.

(a) (b)

Figure 2.7: (a) Original Image, (b) Image Enhancement by FFT

The enhanced image after FFT has the improvements to connect some falsely broken

points on ridges and to remove some spurious connections between ridges. The shown image

at the left side of Figure2.7(b) is also processed with histogram equalization after the FFT

transform.

2.2.3 Fingerprint Image Binarization

Fingerprint Image Binarization is to transform the 8-bit Gray fingerprint image to a 1-

bit image with 0-value for ridges and 1-value for furrows. After the operation, ridges in the

fingerprint are highlighted with black colour while furrows are white. A locally adaptive

Binarization method is performed to binarized the fingerprint image. Such a named method

comes from the mechanism of transforming a pixel value to 1 if the value is larger than the

mean intensity value of the current block (16x16) to which the pixel belongs [Figure 2.8(a)

and Figure 2.8(b)].


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(a) (b)

Figure 2.8: (a) Enhanced image, (b) Image after Binarization

2.3 Fingerprint Image Segmentation

In general, only a Region of Interest (ROI) is useful to be recognized for each

fingerprint image. The image area without effective ridges and furrows is first discarded

since it only holds background information. Then the bound of the remaining effective area is

sketched out since the minutia in the bound region is confusing with those spurious minutia

that are generated when the ridges are out of the sensor [5]. To extract the ROI, a two-step

method is used. The first step is block direction estimation and direction variety check, while

the second is intrigued from some Morphological methods.

2.3.1 Block direction estimation The direction for each block of the fingerprint image with WxW in size(W is 16

pixels by default)is estimated. The algorithm is:

I. The gradient values along x-direction (gx) and y-direction (gy) for each pixel of

the block is calculated. Two Sobel filters are used to fulfill the task.

II. For each block, following formula is used to get the Least Square approximation

of the block direction.

22

2tan

yx

yx

gggg

(2.4)

for all the pixels in each block.


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The formula is easy to understand by regarding gradient values along x-direction and

y direction as cosine value and sine value. So the tangent value of the block direction is

estimated nearly the same as the way illustrated by the following formula.

22 sincos

cossin22tan

(2.5)

After the estimation of each block direction, those blocks without significant

information on ridges and furrows are discarded based on the following formulas:

2222 /2 yxyxyx ggWWggggE (2.6)

For each block, if its certainty level E is below a threshold, then the block is regarded

as a background block. The direction map is shown in the following diagram (assuming there

is only one fingerprint in For each block, if its certainty level E is below a threshold, then the

block is regarded each image.)

(a) (b)

Figure 2.9: (a) Binarization image, (b) Direction Map

2.3.2 ROI extraction by Morphological operations Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted[5,6].

The ‘OPEN’ operation can expand images and remove peaks introduced by background


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noise [Figure 2.10(c)]. The ‘CLOSE’ operation can shrink images and eliminate small

cavities [Figure 2.10(b)].

(a) (b)

Fig 2.10(d) show the interest fingerprint image area and its bound. The bound is the

subtraction of the closed area from the opened area. Then the algorithm throws away those

leftmost, rightmost, uppermost and bottommost blocks out of the bound so as to get the

tightly bounded region just containing the bound and inner area.

(c) (d)

Figure 2.10: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound

2.4 POST PROCESSING STAGE

2.4.1 Minutia Extraction Ridge Thinning is to eliminate the redundant pixels of ridges till the ridges are just

one pixel wide. An iterative, parallel thinning algorithm is used. In each scan of the full


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fingerprint image, the algorithm marks down redundant pixels in each small image

window(3x3). And finally removes all those marked pixels after several scans. The thinned

ridge map is then filtered by other three Morphological operations to remove some H breaks,

isolated points and spikes.

2.4.2 Minutia Marking After the fingerprint ridge thinning, marking minutia points is relatively easy. The

concept of Crossing Number (CN) is widely used for extracting the minutiae [6].

In general, for each 3x3 window, if the central pixel is 1 and has exactly 3 one-value

neighbors, then the central pixel is a ridge branch [Fig 2.11(a)]. If the central pixel is 1 and

has only 1 one-value neighbour, then the central pixel is a ridge ending [Fig 2.11(b)] ,i.e., if

Cn(P) = =1 it’s a ridge end and if Cn(P) = = 3 it’s a ridge bifurcation point, for a pixel P.

(a) (b)

(c)

Figure 2.11: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch

0 1 0

0 1 0

1 0 1

0 0 0

0 1 0

0 0 1

0 1 0

0 1 1

1 0 0


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Fig 2.11(c) illustrates a special case that a genuine branch is triple counted. Suppose

both the uppermost pixel with value 1 and the rightmost pixel with value 1 have another

neighbour outside the 3x3 window, so the two pixels will be marked as branches too. But

actually only one branch is located in the small region. So a check routine requiring that none

of the neighbors of a branch are branches is added.

Also the average inter-ridge width D is estimated at this stage. The average inter ridge

width refers to the average distance between two neighbouring ridges. The way to

approximate the D value is to scan a row of the thinned ridge image and sum up all pixels in

the row whose value is one. Then divide the row length with the above summation to get an

inter ridge width. For more accuracy, such kind of row scan is performed upon several other

rows and column scans are also conducted, finally all the inter-ridge widths are averaged to

get the D.

Together with the minutia marking, all thinned ridges in the fingerprint image are

labelled with a unique ID for further operation. The labelling operation is realized by using

the Morphological operation. Minutiae extraction from thinned image in [Figure 2.12(b) and

Figure 2.12(a).]

(a) (b)

Figure 2.12: (a) Thinned Image, (b) Figure after Minutiae Extraction

2.4.3 False Minutiae Removal At this stage false ridge breaks due to insufficient amount of ink & ridge cross

connections due to over inking are not totally eliminated. Also some of the earlier methods


15

introduce some spurious minutia points in the image. So to keep the recognition system

consistent these false minutiae need to be removed.

Here we first calculate the inter ridge distance D which is the average distance between two

neighbouring ridges. For this scan each row to calculate the inter ridge distance using the

formula:

Inter ridge distance =sum of all pixel with value 1

row length

Finally an averaged value over all rows gives D.

All we label all thinned ridges in the fingerprint image with a unique ID for further operation

using a MATLAB morphological operation BWLABEL.

Now the following 7 types of false minutia points are removed using these steps

(Figure 2.13).

m1 m2 m3 m4

m5 m6 m7

Figure 2.13: False Minutia Points

1. If d(bifurcation, termination) < D & the 2 minutia are in the same ridge then remove

both of them (case m1)

2. If d(bifurcation, bifurcation) < D & the 2 minutia are in the same ridge them remove

both of them (case m2, m3)

3. If d(termination, termination) ≈ D & the their directions are coincident with a small

angle variation & no any other termination is located between the two terminations

then remove both of them (case m4, m5, m6)

4. If d(termination, termination) < D & the 2 minutia are in the same ridge then remove

both of them (case m7)


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2.4.4 Unify Terminations and Bifurcation

Since various data acquisition conditions such as impression pressure can easily

change one type of minutia into the other, most researchers adopt the unification

representation for both termination and bifurcation. So each minutia is completely

characterized by the following parameters at last: 1) x-coordinate, 2) y-coordinate, and 3)

orientation.

The orientation calculation for a bifurcation needs to be specially considered. All

three ridges deriving from the bifurcation point have their own direction. The bifurcation is

broken into three terminations. The three new terminations are the three neighbour pixels of

the bifurcation and each of the three ridges connected to the bifurcation before is now

associated with a termination respectively [6,7][Figure 2.14].

Figure 2.14: A Bifurcation to Three Terminations

Three neighbors become termination(Left)

Each termination has their own orientation(right)

And the orientation of each termination (tx,ty) is estimated by following method :

A ridge segment is tracked whose starting point is the termination and length is D. All

coordinates of points in the ridge segment are summed up. The above summation is then

divided with D to get sx. And sy can be obtained using the same way.

The direction is obtained from:

atan ((sy-ty)/(sx-tx)) (2.7)

0 0 1

1 1 0

0 0 1


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2.5 Minutia Matching

Given two set of minutia of two fingerprint images, the minutia match algorithm

determines whether the two minutia sets are from the same finger or not. An alignment-based

match algorithm is used. It includes two consecutive stages: one is alignment stage and the

second is match stage [7]. 1. Alignment stage. Given two fingerprint images to be matched, any one

minutia from each image is chosen, and the similarity of the two ridges

associated with the two referenced minutia points is calculated. If the

similarity is larger than a threshold, each set of minutia is transformed to a

new coordination system whose origin is at the referenced point and whose x-

axis is coincident with the direction of the referenced point.

2. Match stage: After obtaining two set of transformed minutia points, the elastic

match algorithm is used to count the matched minutia pairs by assuming two

minutia having nearly the same position and direction are identical.

2.5.1 Alignment Stage The ridge associated with each minutia is represented as a series of x-coordinates

(x1,x2…xn) of the points on the ridge. A point is sampled per ridge length L starting from the

minutia point, where the L is the average inter-ridge length. And n is set to 10 unless the total

ridge length is less than 10*L.

So the similarity of correlating the two ridges is derived from:

5.0

0

22

0

m

iii

m

iii

Xx

XxS (2.8)

Where (xi~xn) and (Xi~XN) are the set of minutia for each fingerprint image

respectively. And m is minimal one of the n and N value. If the similarity score is larger than

0.8, then the next step is executed else the next pair of ridges are continued to match. For

each fingerprint, all other minutia are translated and rotated with respect to the reference

minutia according to the following formula:


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푥푖_푛푒푤푦푖_푛푒푤휃푖_푛푒푤

= 푇푀∗푥푖 − 푥푦푖 − 푦휃푖 − 휃

(ퟐ.ퟗ)

Where ,, yx the parameters of the reference minutia and TM are is

TM =cosθ −sinθ 0sinθ cosθ 0

0 0 1 (ퟐ.ퟏퟎ)

The following diagram illustrates the effect of translation and rotation: X’-axis Y-axis X F E y Y’-axis D D X-axis Figure 2.15: Effect of Translation and Rotation

The new coordinate system is originated at minutia F and the new x-axis is coincident

with the direction of minutia F. No scaling effect is taken into account by assuming two

fingerprints from the same finger have nearly the same size.

2.5.2 Match Stage The matching algorithm for the aligned minutia patterns needs to be elastic since the

strict match requiring that all parameters ,, yx are the same for two identical minutiae is

impossible due to the slight deformations and inexact quantization of minutia [8].

The elastic matching of minutia is achieved by placing a bounding box around each

template minutia. If the minutia to be matched is within the rectangle box and the direction

Ө


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discrepancy between them is very small, then the two minutiae are regarded as a matched

minutia pair. Each minutia in the template image either has no matched minutia or has only

one corresponding minutia.

The final match ratio for two fingerprints is the number of total matched pair over the

number of minutia of the template fingerprint. The score is 100*ratio and ranges from 0 to

100. If the score is larger than a pre-specified threshold, the two fingerprints are from the

same finger.

However, the elastic match algorithm has large computation complexity and is

vulnerable to spurious minutia.

푚푎푡푐ℎ 푠푐표푟푒 =푛푢푚(푚푎푡푐ℎ푒푑 푚푖푛푢푡푖푎푒)

max(푛푢푚 표푓 푚푖푛푢푡푖푎푒 푖푛 푖1 푎푛푑 푖2) (ퟐ.ퟏퟏ)


20

Chapter- 3

3. Results and Discussions

A fingerprint image is taking as an input from FVC2000 (Fingerprint Verification

competition 2000) to test the experiment performance [8]. This algorithm is used for

differentiating fingerprints by performing minutiae extraction.

3.1 Results for Minutiae Extraction algorithm

STEP1: First we take original fingerprint image and performing image enhancement by using

Histogram equalization. Figure 3.1(a) shows the original fingerprint image and its

corresponding histogram Equalized image is shown in Figure 3.1(b)

(a) (b)

Figure 3.1: (a) Original Image, (b) Image after Histogram Equalization


21

STEP2: For better enhancement we use histogram Equalization image as an input to the FFT

algorithm. Histogram equalized image is shown in Figure 3.2(a) and its enhanced image after

FFT is shown in Figure 3.2(b).

(a) (b)

Figure 3.2: (a) Histogram image, (b) Image Enhancement using FFT

STEP3: The enhanced image is binarized using Binarization algorithm. Enhanced image is

shown in Figure 3.3(a) and corresponding Binarized image shown in Figure 3.3((b).

(a) (b)

Figure 3.3: (a) FFT Image, (b) Image after Binarization


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STEP4: Binarized image is shown in Figure 3.4(a) and its corresponding directional map

shown in Figure 3.4(b).

(a) (b)

Figure 3.4: (a) Binarization image, (b) Direction map of Binarization Image

STEP5: For extracting the un necessary area we use ROI algorithm. Binarized image is

shown in 3.5(a), and Its corresponding ROI image shown in 3.5(b).The size of binarized

image 256*256, but the size of ROI image is 224*224.

(a) (b)

Figure 3.5: (a) Binarization Image, (b) ROI Image


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STEP6: Figure 3.6(a) shows the open area of the ROI Image and Figure 3.6(b) shows the

close area of the ROI Image.

(a) (b)

Figure 3.6: (a) Open Operation, (b) Close Operation

STEP7: After Binarization the image is an input to the region of interest algorithm. it shows

the actual area of the fingerprint image by bounding the region with some useful colours in

Figure 3.7(b).

(a) (b)

Figure 3.7: (a) Adaptive Binarization, (b) ROI+BOUND image


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STEP8: From ROI+BOUND image we found the ROI image. We use ROI image is an input

to the thinned algorithm. The Binarized image is shown in Figure 3.8(a) and its

corresponding image is shown in Figure 3.8(b). From the thinned figure We conclude that

Bifurcation and termination points are shown clearly.

(a) (b)

Figure 3.8: (a) ROI image, (b) Thinned Image STEP9: We apply thinned image an input to the minutiae extraction algorithm for finding

minutiae point. Figure 3.9(a) shows thinned Image and Figure 3.9(b) shows minutiae marked

thinned image. Here red dots show the termination (ridge ending) points and yellow dots show

the bifurcation points.

(a) (b)

Figure 3.9: (a) Thinned Image, (b) Minutiae Marking after Thinning


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3.2 Comparison Results For Minutiae matching

For simulation result, we have taken the threshold value of 0.65.

3.2.1 Two Different Fingerprints Figure3.10 (a), Figure 3.10(b) shows two different fingerprint and Figure 3.10(c),

Figure 3.10(d) shows corresponding minutiae marked image. The match score value between

the two images is 0.37. As this value is less than the threshold value, we can conclude that

these two fingerprints are of two different persons.

(a) (b)

(c) (d)

Figure 3.10: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint, (d) Minutiae Extraction of Second Fingerprint


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3.2.2 Two Fingerprint of a same person with a Little Difference

Figure3.11 (a), Figure 3.11(b) shows two different fingerprint, here Figure 3.11(b)

shows the little modify of Figure 3.11(a). Figure 3.10(c), Figure 3.10(d) shows corresponding

minutiae marked image. The match score value between the two images is 0.68. As this value

is Greater than the threshold value, we can conclude that these two fingerprints are of a same

person.

(a) (b)

(c) (d)

Figure 3.11: (a) First Fingerprint, (b) Second Fingerprint, (c) Minutiae extraction of First Fingerprint,

(d) Minutiae extraction of Second Fingerprint


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Chapter- 4

4. Conclusion

The above implementation was an effort to understand how Fingerprint Recognition

is used as a form of biometric to recognize identities of human beings. It includes all the

stages from enhancement to minutiae extraction of fingerprints. There are various standard

techniques are used in the intermediate stages of processing.

The relatively low percentage of verification rate as compared to other forms of

biometrics indicates that the algorithm used is not very robust and is vulnerable to effects like

scaling and elastic deformations. Various new techniques and algorithm have been found out

which give better results.

Also a major challenge in Fingerprint recognition lies in the pre processing of the bad

quality of fingerprint images which also add to the low verification rate.

The reliability of any automatic fingerprint system strongly relies on the precision

obtained in the minutia extraction process. A number of factors are detrimental to the correct

location of minutia. Among them, poor image quality is the most serious one. In this project,

we have combined many methods to build a minutia extractor and a minutia matcher. The

following concepts have been used- segmentation using Morphological operations, minutia

marking by specially considering the triple branch counting, minutia unification by

decomposing a branch into three terminations.

There is a scope of further improvement in terms of efficiency and accuracy which can be

achieved by improving the hardware to capture the image or by improving the image

enhancement techniques. So that the input image to the thinning stage could be made better

this could improve the future stages and the final outcome.


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References

[1] Fingerprint database - FVC2002 (Fingerprint Verification Competition 2002)

[2] Rafael C .Gonzalez, Richard E Woods digital image processing”2nd edition, 2002.

[3] K. Jain, F. Patrick, A. Arun , “Handbook of Biometrics”, Springer Science Business

Media, LLC, 1st edition, pp. 1-42, 2008.

[4] D. Maio, and D. Maltoni, “Direct gray-scale minutia detection in fingerprints”, IEEE

Transactions Pattern Analysis and Machine Intelligence, vol. 19(1), pp. 27-40, 1997.

[5] D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, “4.3: Minutiae-based Methods’

(extract) from Handbook of Fingerprint Recognition”, Springer, New York, pp. 141-

144, 2003.

[6] E. Hastings, “A Survey of Thinning Methodologies”, Pattern analysis and Machine

Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-885, 1992.

[7] K. Nallaperumall, A. L. Fred, and S. Padmapriya, “A Novel Technique for Fingerprint

Feature Extraction Using Fixed Size Templates”, IEEE 2005 Conference, pp. 371-

374, 2005.

[8] P. Komarinski, P. T. Higgins, and K. M. Higgins, K. Fox Lisa, “Automated Fingerprint

Identification Systems (AFIS)”, Elsevier Academic Press, pp. 1-118, 2005.


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