Biometric Fingerprint Recognition based on Minutiae Matching

A project work

Submitted to the Department of Information & Communication Technology,

Comilla University in partial fulfillment of the requirement for the degree of

Bachelor of Science (Engg.) in Information & Communication Technology

Submitted By

Nabila Mahjabin

Exam Roll: 1009035

Registration No: SC-ICT-11-800065

Session: 2010-2011

Supervised By

Orvila Sarker

Lecturer

DEPARTMENT OF INFORMATION & COMMUNICATION TECHNOLOGY

COMILLA UNIVERSITY

KOATBARI-3506, BANGLADESH

December,2016

i

Acknowledgement

At first, I would like to give thanks to Almighty for offering such an opportunity to

perform my responsibilities as a B.Sc (Engg.) student and complete my project report

within the schedule time appropriately.

I would like to convey my gratitude to my supervisor, Orvila Sarker, Lecturer,

Department of Information & Communication Technology for her constant guidance,

advice and encouragement as well as her benign support for the completion of the

project. I greatly acknowledge her mastermind direction, constant supervision, optimistic

counseling and unremitting backup to carry out the project and prepare this report

successfully.

I am cordially grateful to Dulal Chakraborty, Assistant Professor and chairman,

Department of Information & Communication Technology, for his continuous support

and suggestions and guidelines from the beginning of my university life.

I also acknowledge to all of the teachers of the Department of Information &

Communication Technology for sharing their views about the project.

Finally it is with pleasure that I want to express my gratitude to my beloved family and

friends for offering their consistent support to me in many ways.

Author

ii

Certificate

This is to certify that the project report entitled “Biometric Fingerprint Recognition

based on Munitiae Matching” submitted by Nabila Mahjabin in partial fulfillment in the

requirements of the degree of B.Sc. in Engineering in Information & Communication

Technology, faculty of Engineering, Comilla University, is an authentic work carried out

by the author under my direct supervision and guidance. I approve the style and contents

of this project.

Orvila Sarker

Lecturer

Department of Information &

Communication Technology

Comilla University

DEPARTMENT OF

INFORMATION & COMMUNICATION TECHNOLOGY

COMILLA UNIVERSITY

iii

Abstract

In biometric system, the fingerprint recognition has been researched for the long period

of time and it has shown the most promising future in the real world application.

However, because of the complex distortions among the different impression of the same

finger in real life, fingerprint recognition is still a challenging problem. The study and

implementation of a Biometric fingerprint recognition system based on Minutiae

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 recognition through extracting and matching minutiae. To achieve

good minutiae extraction in fingerprints with varying quality, preprocessing in form of

image enhancement and binarization is first applied on fingerprints before they are

evaluated. Many methods have been combined to build a minutiae extractor and a

minutiae matcher. Minutiae-marking with false minutiae removal methods are used in the

work. An alignment-based elastic matching algorithm has been developed for minutiae

matching. This algorithm is capable of finding the correspondences between input

minutiae pattern and the stored template minutiae pattern without resorting to exhaustive

search. Performance of the developed system is then evaluated on a database with

fingerprints from different people. One of the limitations of existing fingerprint

recognition techniques is that these systems tend to fall short when the available

fingerprint is of low quality.

iv

Table of Contents

Chapter Title Page

1.Introduction

1.1 Introduction

1.2 Fingerprint

1.3 Importance of Fingerprint

1.3.1 Live Scan Fingerprints

1.3.2 Latent Fingerprints

1.3.3 Patent Fingerprints

1.4 Fingerprint pattern types

1.4.1 Loop Patterns

1.4.2 Arch Pattern

1.4.3 Whorl Patterns

1.5. Fingerprint Recognition

1.5.1. One to One Matching

1.5.2. One to Many Matching

1.6. Fingerprint Matching Techniques

1.6.1 Minutiae-based matching

1.6.2 Pattern-based (or image-based) matching

1.6.3 Correlation Based Matching

1-8

2

2

3

3

3

3

3

3

4

5

6

6

6

7

7

7

7

2. Biometrics

2.1. Biometrics

2.2. Leading Biometric Technologies

2.2.1 Fingerprint Recognition

2.3. Fingerprints as a Biometric

2.3.1 Fingerprint Representation

2.3.2 Minutiae

9-13

10

10

10

12

12

13

3. Fingerprint Recognition

3.1. Fingerprint Recognition Technique

3.2. Block diagram of Fingerprint Recognition system

14-17

15

15

4.Implementation

4.1 Algorithm level design

4.2. Definitions

4.3. Image Acquisition

4.4 Fingerprint Image Enhancement Technique

4.4.1 Histogram Equalization

4.4.2 Fingerprint Enhancement by Fourier transform

4.4.3 Fingerprint Image Binarization

4.5 Fingerprint Image Segmentation

18-32

19

19

21

21

21

22

24

24

v

Chapter Title Page

4.5.1 Block direction estimation

4.5.2 ROI extraction by Morphological operations

4.6 Post processing stage

4.6.1 Minutiae Extraction

4.6.2 Minutiae Marking

4.6.3 False Minutiae Removal

4.6.4 Unify Terminations and Bifurcation

4.7 Minutiae Matching

4.7.1 Alignment Stage

4.7.2 Match Stage

25

25

27

27

27

27

30

30

31

32

5.Results & Discussion

5.1 Simulation Results

33-36

34

6: Conclusion 37-38

References 39-40

vi

List of Figures

Figure No. Title Page

Figure 1.1 A fingerprint image acquired by an optical sensor 2

Figure 1.2 (a)Ulnar Loop (b)Radial Loop 4

Figure 1.3 (a)Plain Arch (b)Tented Arch 5

Figure 1.4 (a)Plain Whorl (b) Central Pocket Whorl (c) Double

Whorl (d) Accidental Whorl

5

Figure 2.1 Verification vs. Identification 11

Figure 2.2 Fingerprint as a Biometric 12

Figure 2.3 Minutiae points 13

Figure 3.1 Fingerprint Recognition System 16

Figure 4.1 Steps involved in fingerprint Recognition 20

Figure 4.2 (a) Histogram of an image, (b) Histogram equalization of

an image

22

Figure 4.3 (a) Original image, (b) Enhanced Image after Equalization 22

Figure 4.4 (a) Original Image, (b) Image Enhancement by FFT 23

Figure 4.5 (a) Enhanced image, (b) Image after Binarization 24

Figure 4.6 (a) Binarization image, (b) Direction Map 26

Figure 4.7 (a) Original Image, (b) Close Operation, (c) Open

Operation, (d) ROI+Bound

26

Figure 4.8 (a)Bifurcation (b)Termination (c)Triple counting branch 28

Figure 4.9 (a) Thinned Image, (b) Figure after Minutiae Extraction 28

Figure 4.10 False Minutiae Points 29

Figure 4.11 A Bifurcation to Three Terminations 30

Figure 4.12 Effect of Translation and Rotation 32

Figure 5.1 Original image with its minutiae 34

Figure 5.2 Similarity between two fingerprints 35

Figure 5.3 Relation between FMR and FNMR 36

1

Chapter 1

Introduction

2

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. Fingerprint

A fingerprint is the feature pattern of one finger. It is an imprints formed by the friction

ridges of the skin and thumbs. They have long been used for identification because of

their immutability and individuality. Immutability refers to the permanent and

unchanging character of the pattern on each finger. Individuality refers to the uniqueness

of ridge details across individuals; the uniqueness of a fingerprint can be determined by

the pattern of ridges and furrows as well as by features called minutiae, which are some

abnormal points on the ridges. Figure 1.1 shows a Fingerprint image acquired by an

optical sensor. However, shown by intensive research on fingerprint recognition,

fingerprints are not distinguished by their ridges, but by the minutiae points.

Figure 1.1: A fingerprint image acquired by an optical sensor

3

1.3 Importance of Fingerprint:

Fingerprint is a very interesting and unique feature of the human body, because the

Fingerprints can uniquely identify the person and it does not changes from birth to

death. Even twines‟ fingerprints do not match. Due to persistent and uniqueness property

of fingerprint it has been implementing in many areas such as UID Card, Passport,

Criminal Data, etc. Fingerprints are unique pattern of ridges (raised) and furrows

(recessed). That appears on the pads of fingers and thumbs. Fingerprints Acquisition

Techniques are categorized into three types [1].

1.3.1 Live Scan Fingerprints: Live Scan Fingerprints are Impressed Fingerprints

sometimes known as Plastic Prints. The quality of Fingerprint image received is better

than Patent Fingerprint. It can be retrieve using fingerprint scanner, Clay, Wax, and Paint.

1.3.2 Latent Fingerprints: Latent fingerprint is invisible to the eye directly and

requires enhancement technique and instruments to check. To extract the fingerprint from

the surface basic powder and chemical techniques are used.

1.3.3 Patent Fingerprints: Patent Fingerprint is also called as the Visible

Fingerprint. It is formed intentionally by person to proof their identity. At identification

stage, it is still use to record presence of person. Patent Fingerprints can be created by

blood, grease, ink, or dirt etc.

1.4 Fingerprint pattern types

Fingerprint patterns are divided into three main groups consisting of Arches,

Loops and Whorls. Approximately 5% of all fingerprints are Arches, 30% are

Whorls and 65% are Loops.

1.4.1 Loop Patterns:

In a Loop pattern, the ridges will flow in one side, re-curve, (loop around) touch or pass

through an imaginary line drawn from the delta to the core, and exit the pattern on

the same side from which it entered. The loop pattern consists of one or more re-curving

ridges and one delta. Figure 1.2 shows the two types of loop pattern in a fingerprint

image. They are:

a. Ulnar loop

b. Radial loop.

The loop is towards the right or Ulnar bone known as “Ulnar loop”.

4

The loop is towards the left or Radial bone is known as “Radial Loop”.

Difference between ulnar and radial loop are, if the ridges flow in from the little

finger side, this would be an ulnar loop and if the ridges flow in from the thumb side this

would be a radial loop.

Figure 1.2:(a)Ulnar Loop (b)Radial Loop

1.4.2 Arch Pattern:

In an arch pattern, ridges flow in one side and flow out the opposite side. There are no

deltas in an arch pattern. Figure 1.3 shows the two types of loop pattern in a fingerprint

image [1].They are:

a. Plain arch,

b. Tented arch.

Plain arches have a flow of ridges from one side to the other of the pattern, no

“significant up thrusts” and the ridges enter on one side of the impression, and flow

out the other with a rise or wave in the centre. Tented arches have an angle, an up

thrust, or two of the three basic characteristics of the loop.

5

Figure 1.3(a)Plain Arch (b)Tented Arch

1.4.3 Whorl Patterns:

Any fingerprint pattern which contains two or more deltas will be a whorl pattern. A

whorl pattern consists of a series of almost concentric circles. Figure 1.4 shows the four

types of loop pattern in a fingerprint image.They are:

a) Plain whorl

b) Central Pocket Loop whorl

c) Double Loop Whorl

d) Accidental Whorl

Figure 1.4: (a)Plain Whorl (b)Central Pocket Whorl (c)Double Whorl (d)Accidental

Whorl

6

Plain whorls consist of one or more ridges which make a complete circuit with

two deltas, between at least one re-curving ridges within the inner pattern area is cut

or touched.

Central pocket loop whorls consist of at least one re-curving ridge to the line of

flow, with two deltas, between which when an imaginary line is drawn, no re-curving

ridge within the pattern area is cut or touched.

Double loop whorls consist of two separate and distinct loop formations with two

separate and distinct shoulders for each core, two deltas and one or more ridges

which make, a complete circuit.

The accidental pattern will contain two points of delta. One delta will be related to a re-

curve and the other will be related to an up thrust.

1.5. Fingerprint Recognition

Fingerprint recognition is also called as fingerprint authentication. It is a most popular

biometric solution, refers to the automated method to confirmation the identity.

Fingerprint is one of the biometrics features that are unique to each human. Fingerprints

are rich with details, consisting of many ridges and furrows [2]. Many modern security

systems utilize this uniqueness for identity recognition and verification purposes. Both

systems work based on the same fundamentals but in different matching modes:

fingerprint identification or recognition is one-to many matching process, while

fingerprint verification or authentication is a one-to-one matching process.

1.5.1. One to One Matching:- It is applied where Input fingerprint is matched

directly with only one fingerprint, which produce result either matched or not matched.

Example: Secure Login using EmailID, Laptop & Desktop Device Protection, etc.

1.5.2. One to Many Matching:- It is applied to specified areas where number of

applicant store their fingerprint. The Input fingerprint is matched with number of

fingerprint stored to uniquely find person identity. Example: Attendance Management,

Secure Login without using Email ID, etc.

Most existing fingerprint recognition systems have no difficulty in matching high quality

fingerprint images. However, many falls short when the fingerprint images are low in

quality. Due to its cheapest biometric solution fingerprint recognition uses in different

areas such as Physical Access Control and Time & Attendance Management.

7

1.6. Fingerprint Matching Techniques

The large number of approaches to fingerprint matching can be coarsely classified

into three families. They are,

1. The minutiae-based (feature-based) matching,

2. The pattern-based (or image-based) matching, and

3. The correlation-based matching.

1.6.1 Minutiae-based matching: This is the most popular and widely used

technique, being the basis of the fingerprint comparison made by fingerprint

examiners. Minutiae are extracted from the two fingerprints and stored as sets of

points in the two- dimensional plane. Minutiae-based matching essentially consists

of finding the alignment between the template and the input minutiae sets that results

in the maximum number of minutiae pairings.[3]

1.6.2 Pattern-based (or image-based) matching: Pattern based algorithms

compare the basic fingerprint patterns (arch, whorl, and loop) between a previously

stored template and a candidate fingerprint. This requires that the images be aligned in

the same orientation [4]. To do this, the algorithm finds a central point in the

fingerprint image and centers on that. In a pattern-based algorithm, the template

contains the type, size, and orientation of patterns within the aligned fingerprint

image. The candidate fingerprint image is graphically compared with the template to

determine the degree to which they match [5].

1.6.3 Correlation Based Matching: It is advanced Matching Technique

which involves advance image processing methods [6]. The Correlation-based

matching processes to correlate pixel values of fingerprint image instead of

Minutiae. The specific color properties and threshold values are store to correlate

with template stored in the database. It is very helpful when Minutiae information

from fingerprint image is not able to extract. [7]

There have been other techniques introduced for fingerprint matching outside of the core

three techniques, including the principal component analysis technique, which utilizes

statistics theory for computation operation, and the use of neural network.

A wide variety of systems require reliable personal authentication schemes to either

confirm or determine the identity of individuals requesting their services. The purpose of

such schemes is to ensure that the rendered services are accessed by a legitimate user, and

not anyone else. Examples of these systems include secure access to buildings, computer

systems, laptops, cellular phones and ATMs. In the absence of robust authentication

schemes, these systems are vulnerable to the wiles of an impostor.

8

Traditionally, passwords (knowledge-based security) and ID cards (token-based security)

have been used to restrict access to systems. The major advantages of this traditional

personal identification are that

(i) They are very simple

(ii) They can be easily integrated into different systems with a low cost.

However these approaches are not based on any inherent attributes of an individual to

make a personal identification thus having number of disadvantages like tokens may be

lost, stolen, forgotten, or misplaced; PIN may be forgotten or guessed by impostors.

Security can be easily breached in these systems when a password is divulged to an

unauthorized user or a card is stolen by an impostor; further, simple passwords are easy

to guess (by an impostor) and difficult passwords may be hard to recall (by a legitimate

user).Therefore they are unable to satisfy the security requirements of our electronically

interconnected information society. The emergence of biometrics has addressed the

problems that plague traditional verification. [8]

9

Chapter 2

Biometrics

10

2.1. Biometrics

In the world of computer security, biometrics refers to authentication techniques that rely

on measurable physiological and individual characteristics that can be automatically

verified. In other words, we all have unique personal attributes that can be used for

distinctive identification purposes, including a fingerprint, the pattern of a retina, and

voice characteristics. Strong or two-factor authentication— identifying oneself by two of

the three methods of something you know (for example, a password), have (for example,

a swipe card), or is (for example, a fingerprint)—is becoming more of a genuine standard

in secure computing environments. Some personal computers today can include a

fingerprint scanner where we place our index finger to provide authentication. The

computer analyzes our fingerprint to determine who am I, based on our identity followed

by a pass code or pass phrase, allows us different levels of access. Access levels can

include the ability to open sensitive files, to use credit card information to make

electronic purchases, and so on.

2.2. Leading Biometric Technologies

A growing number of biometric technologies have been proposed over the past several

years, but only in the past 5 years have the leading ones become more widely deployed.

Some technologies are better suited to specific applications than others, and some are

more acceptable to users. We describe seven leading biometric technologies:

Facial Recognition

Fingerprint Recognition

Hand Geometry

Iris Recognition

Signature Recognition

Speaker Recognition

2.2.1 Fingerprint Recognition

Fingerprint recognition is one of the best known and most widely used biometric

technologies. Automated systems have been commercially available since the early

1970s, and at the time of our study, we found there were more than 75 fingerprint

recognition technology companies. Until recently, fingerprint recognition was used

primarily in law enforcement applications.

Fingerprint recognition technology extracts features from impressions made by the

distinct ridges on the fingertips. The fingerprints can be either flat or rolled. A flat print

11

captures only an impression of the central area between the fingertip and the first

knuckle; a rolled print captures ridges on both sides of the finger. An image of the

fingerprint is captured by a scanner, enhanced, and converted into a template. Scanner

technologies can be optical, silicon, or ultrasound technologies. Ultrasound, while

potentially the most accurate, has not been demonstrated in widespread use. In 2002, we

found that optical scanners were the most commonly used. During enhancement, “noise”

caused by dirt, cuts, scars, and creases or dry, wet or worn fingerprints is reduced, and the

definition of the ridges is enhanced. Approximately 80 percent of vendors base their

algorithms on the extraction of minutiae points relating to breaks in the ridges of the

fingertips. Other algorithms are based on extracting ridge patterns. Fingerprint

recognition includes two sub-domains: one is fingerprint verification and the other is

fingerprint identification (figure 2.1).

Figure 2.1: Verification vs. Identification

12

2.3. Fingerprints as a Biometric

Among all biometric traits, fingerprints have one of the highest levels of reliability and

have been extensively used by forensic experts in criminal investigations. A fingerprint

refers to the flow of ridge patterns in the tip of the finger. The ridge flow exhibits

anomalies in local regions of the fingertip (Figure2.2), and it is the position and

orientation of these anomalies that are used to represent and match fingerprints.

Figure 2.2: Fingerprint as a Biometric

Although not scientifically established, fingerprints are believed to be unique across

individuals, and across fingers of the same individual. Even identical twins having similar

DNA, are believed to have different fingerprints. Traditionally, fingerprint patterns have

been extracted by creating an inked impression of the fingertip on paper.

2.3.1 Fingerprint Representation

The uniqueness of a fingerprint is determined by the topographic relief of its ridge

structure and the presence of certain ridge anomalies termed as minutiae points.

13

Typically, the global configuration defined by the ridge structure is used to determine the

class of the fingerprint, while the distribution of minutiae points is used to match and

establish the similarity between two fingerprints.

Automatic fingerprint identification systems, that match a query print against a large

database of prints (which can consist of millions of prints), rely on the pattern of ridges in

the query image to narrow their search in the database (fingerprint indexing), and on the

minutiae points to determine an exact match (fingerprint matching). The ridge flow

pattern itself is rarely used for matching fingerprints.

2.3.2 Minutiae

Minutiae, in fingerprinting terms, are the points of interest in a fingerprint, such as

bifurcations and ridge endings (figure 2.3). Examples are:

a.) ridge endings - a ridge that ends abruptly

b.) ridge bifurcation - a single ridge that divides into two ridges

c.) short ridges, island or independent ridge - a ridge that commences, travels a short

distance and then ends

d.) ridge enclosures - a single ridge that bifurcates and reunites shortly afterward to

continue as a single ridge

e.) spur - a bifurcation with a short ridge branching off a longer ridge

f.) crossover or bridge - a short ridge that runs between two parallel ridges

Fig 2.3: Minutiae points

14

Chapter 3

Fingerprint Recognition

15

3.1. Fingerprint Recognition Technique

Fingerprints have been widely used because of their high acceptability, immutability,

individuality and acceptability. A good fingerprint contains 25 to 80 minutiae points.

Minutiae based fingerprint recognition consists of Thinning, Minutiae extraction,

Minutiae matching and Computing matching score. The major steps involved in

automated fingerprint recognition include

a) Fingerprint Acquisition,

b) Fingerprint Segmentation,

c) Fingerprint Image Enhancement,

d) Feature Extraction,

e) Minutiae Matching,

f) Fingerprint Classification

3.2. Block diagram of Fingerprint Recognition system

Fingerprint recognition (also known as Dactyloscopy) is the process of comparing known

fingerprint against another or template fingerprint to determine if the impressions are

from the same finger or not. It includes two sub-domains: one is fingerprint verification

and the other is fingerprint identification [9].

Verification specify an individual fingerprint by comparing only one fingerprint template

stored in the database, while identification specify comparing all the fingerprints stored in

the database. Verification is one to one matching and identification is one to N (number

of fingerprint templates available in database) matching [10]. Verification is a fast

process as compared to identification.

Figure 3.1 shows the complete structure of fingerprint recognition system. First of all we

take a fingerprint image. After taking an input image we can apply fingerprint

segmentation technique. Segmentation is separation of the input data into foreground

(object of interest) and background (irrelevant information). Before extracting the feature

of a fingerprint it is important to separate the fingerprint regions (presence of ridges)

from the background. This is very useful for recovering false feature extraction. In some

cases, a correct segmentation is very difficult, especially in poor quality fingerprint image

or noisy images. Orientation field plays an important role in fingerprint recognition

system. Orientation field consist of four major steps:

(1) Pre-processing fingerprint image

(2) Determining the primary ridges of fingerprint block

(3) Estimating block direction by projective distance variance of such a ridge

(4) Correcting the estimated orientation field. Image enhancement is use to improve

significantly the image quality by applying some image enhancement technique.

16

Fig 3.1: Fingerprint Recognition System

17

The main purpose of such procedure is to enhance the image by improving the clarity of

ridge structure or increasing the consistency of the ridge orientation. Fingerprint

classification is used to check the fingerprint pattern type. After classification of

fingerprint we can apply fingerprint ridge thinning which is also called block filtering, it

is used to reduce the thickness of all ridges lines to a single pixel width. Thinning does

not change the location and orientation of minutiae points compared to original

fingerprint which ensures accurate estimation of minutiae points. Then we can extract

minutiae points and generate data matrix. Finally we can use minutiae matching to

compare the input fingerprint data with the template data and give the result.

18

Chapter 4

Implementation

19

4.1 Algorithm level design

In this section the definitions and FRMSM (Fingerprint recognition using minutiae score

matching) model are discussed. Figure (4.1) gives the algorithm level design flow chart

of FRMSM which is used to match the test fingerprint with the template database using

Minutia Matching Score.

4.2. Definitions:

Termination: The location where a ridge comes to an end.

Bifurcation: The location where a ridge divides into two separate ridges.

Binarization: The process of converting the original grayscale image to a black-and white

image.

Thinning:The process of reducing the width of each ridge to one pixel.

Termination Angle: The angle between the horizontal and the direction of the ridge.

Bifurcation Angle: The angle between the horizontal and the direction of the valley

ending between the bifurcations.

False Matching Ratio: It is the probability that the system will decide to allow

access to an (FMR) imposter is given in an equation

𝐹𝑀𝑅 =FalseMat ches

𝐼𝑚 𝑝𝑜𝑠𝑡𝑒𝑟 𝐴𝑡𝑡𝑒𝑚𝑝𝑡𝑠 …. (4.1)

The imposter attempts are implemented by matching each input image with all the

template images.

False match was recorded for each imposter attempt when the matching score was greater

than the established threshold.

False Non Matching Ratio (FNMR): It is the probability that the system denies

access to an approved user is given in an equation

𝐹𝑁𝑀𝑅 =FalseNonMatches

𝐸𝑛𝑟𝑜𝑙𝑙𝑒𝑒 𝐴𝑡𝑡𝑒𝑚𝑝𝑡𝑠 .… (4.2)

Enrollee attempts are implemented by matching each input image with corresponding

template image, hence it is one-to-one matching.

A False Non-match was recorded when the matching score between an enrollee and its

template was less than the established threshold.

20

Figure 4.1: Steps involved in fingerprint Recognition

Load Image

Histogram Equalization

Enhancement using FFT

Ridge detection

Binarization

ROI

Thinning

Minutia Marking

Alignment and Match Template

Matching Score

21

Matching Score: It is used to calculate the matching score between the input and

template data is given in an equation

𝑀𝑎𝑡𝑐𝑕𝑖𝑛𝑔𝑆𝑐𝑜𝑟𝑒 =MatchingMinutiae

𝑀𝑎𝑥 (𝑁𝑇 ,𝑁𝐼) .…(4.3)

Where, NT and NI represent the total number of minutiae in the template and input

matrices respectively. By this definition, the matching score takes on a value between 0

and 1. Matching score of 1 and 0 indicates that data matches perfectly and data is

completely mismatched respectively.

4.3. Image Acquisition

The fingerprint images are captured using the inkless fingerprint sensor (scanner). The

quality of the fingerprint images is very important since it affects directly the minutiae

extraction algorithm.

4.4 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.

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

adjusting the intensity distribution on a histogram [11]. 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 4.2(a), the

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

visualization effect is enhanced (Figure 4.2(b)).

22

Figure 4.2: (a) Histogram of an image, (b) Histogram equalization of an image

(a) (b)

Figure 4.3: (a) Original image, (b) Enhanced Image after Equalization

4.4.2 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

𝑓 𝑢, 𝑣 = ∑∑𝑓 𝑥, 𝑦 × exp −j2π × ux

M+

vy

N .…(4.4)

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 [11] .Where the magnitude of the original

FFT = abs (F (u ,v)) = |F(u ,v)| ….(4.5)

23

The enhanced block is obtained according to:

g (x , y ) =𝐹−1 𝐹(𝑢, 𝑣) × |𝐹 𝑢, 𝑣 |^𝑘 ….(4.6)

Where 𝐹−1 (F(u,v)) is done by:

F(x,y)=1

𝑀𝑁 ∑ ∑ 𝐹(𝑢, 𝑣) × 𝑒𝑥𝑝 2𝜋𝑗 ×

𝑢𝑥

𝑀+

𝑣𝑦

𝑁 𝑁−1

𝑦=0𝑀−1𝑥=0 .…(4.7)

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

The k in formula 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.

Figure 4.4: (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

24

image at the left side of Figure4.4(b) is also processed with histogram equalization

after the FFT transform.

4.4.3 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 color 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 4.5(a) and Figure 4.5(b)].

Figure4.5: (a) Enhanced image, (b) Image after Binarization

4.5 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 minutiae in the bound region is confusing with

those spurious minutiae that are generated when the ridges are out of the sensor

[12]. 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.

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4.5.1 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.

𝑇𝑎𝑛β=2∑∑ gx ×gy

∑∑ g x2 −g y

2 .…(4.8)

for all the pixels in each block.

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.

𝑇𝑎𝑛2𝜃 =2sin θ .cos θ

cos 2 𝜃−sin 2 𝜃 ….(4.9)

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

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

E= 2∑∑ 𝑔𝑥 × 𝑔𝑦 + ∑∑(𝑔 𝑥2 − 𝑔 𝑦

2 ) /𝑊 × 𝑊∑∑(𝑔 𝑥2 + 𝑔 𝑦

2 ) ….(4.10)

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 figure 4.6 (b) (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.)

4.5.2 ROI extraction by Morphological operations

Two Morphological operations called „OPEN‟ and „CLOSE‟ are adopted[12,13].The

„OPEN‟ operation can expand images and remove peaks introduced by background

noise [Figure 4.7(c)]. The „CLOSE‟ operation can shrink images and eliminate small

cavities [Figure 4.7(b)]. Fig 4.7(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.

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Figure 4.6: (a) Binarization image, (b) Direction Map

Figure 4.7: (a) Original Image, (b) Close Operation, (c) Open Operation, (d) ROI+Bound

27

4.6: Post processing stage

4.6.1 Minutiae 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 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.

4.6.2 Minutiae Marking

After the fingerprint ridge thinning, marking minutiae points is relatively easy.

The concept of Crossing Number (CN) is widely used for extracting the minutiae [13]. 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 4.8(a)]. If the central pixel is 1

and has only 1 one-value neighbour, then the central pixel is a ridge ending [Fig

4.8(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.

Figure 4.8(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 actuallyonly 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 minutiae marking, all thinned ridges in the fingerprint image

are labeled with a unique ID for further operation. The labeling operation is realized

by using the Morphological operation.Minutiae extraction from thinned image in

[Figure4.9 (b) and Figure 4.9(a).]

4.6.3 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 introduce some spurious minutiae points in the image. So to keep the

recognition system consistent these false minutiae need to be removed.

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Figure 4.8: (a) Bifurcation, (b) Termination, (c) Triple Counting Branch

Figure 4.9: (a) Thinned Image, (b) Figure after Minutiae Extraction

29

Here 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 ….(4.11)

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 minutiae points are removed using these steps

(Figure 4.10).

Figure 4.10: False Minutiae 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)

30

4.6.4 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

neighbor pixels of the bifurcation and each of the three ridges connected to the

bifurcation before is now associated with a termination respectively [13,14][Figure

4.11].

Figure 4.11: 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 get𝑠𝑥 . And 𝑠𝑦 can be obtained using the same way.

The direction is obtained from:

a𝑡𝑎𝑛 𝑠𝑦 − 𝑡𝑦 / 𝑠𝑥 − 𝑡𝑥 ….(4.12)

4.7 Minutia Matching

Given two set of minutiae of two fingerprint images, the minutiae match

algorithm determines whether the two minutiae 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 [15].

1. Alignment stage: Given two fingerprint images to be matched, any one minutiae

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

31

the two referenced minutiae points is calculated. If the similarity is larger than a

threshold, each set of minutiae 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 minutiae points, the elastic match

algorithm is used to count the matched minutiae pairs by assuming two minutiae having

nearly the same position and direction are identical.

4.7.1 Alignment Stage

The ridge associated with each minutiae 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 minutiae 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:

𝑠 =∑ xi X i

mi=0

∑ x2i

mi=0 X2

i 0.5

.… (4.13)

Where (xi~xn) and (Xi~XN) are the set of minutiae 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 minutiae are translated and rotated with

respect to the reference minutiae according to the following formula:

𝑥𝑖_𝑛𝑒𝑤𝑦𝑖_𝑛𝑒𝑤𝜃𝑖_𝑛𝑒𝑤

= 𝑇𝑀 ∗ 𝑥𝑖_𝑥𝑦𝑖_𝑦𝜃𝑖_𝜃

….(4.14)

Where (x,y,𝜃)the parameters of the reference minutiae and TM are is

TM= cos𝜃 − sin 𝜃 0sin 𝜃 cos𝜃 0

0 0 1 ….(4.15)

The diagram 4.12 illustrates the 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.

32

Figure 4.12: Effect of Translation and Rotation

4.7.2 Match Stage

The matching algorithm for the aligned minutiae patterns needs to be elastic since the

strict match requiring that all parameters (x,y,𝜃)are the same for two identical minutiae

is impossible due to the slight deformations and inexact quantization of minutia [16]. The elastic matching of minutiae is achieved by placing a bounding box around

each template minutiae. If the minutiae to be matched is within the rectangle box

and the direction 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 minutiae of thetemplate 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 minutiae.

𝑀𝑎𝑡𝑐𝑕 𝑆𝑐𝑜𝑟𝑒 =num (matched minutia )

𝑚𝑎𝑥 (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑢𝑛𝑖𝑡𝑖𝑎 𝑖𝑛 𝑖𝑚𝑎𝑔𝑒 1 𝑎𝑛𝑑 𝑖𝑚𝑎𝑔𝑒 2) (4.16)

33

Chapter 5

Results & Discussion

34

5.1 Simulation Results

A fingerprint image is taking as an input from FVC2002 (Fingerprint Verification

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

differentiating fingerprints by performing minutiae extraction. Figure 5.1 shows the

original image of a fingerprint from the database and its minutiae points. Figure 5.2

shows the similarities between two fingerprints. Figure 5.3 shows the relationship

between FMR and FNMR. A system‟s false match rate (FMR) and false nonmatch rate

(FNMR) depend on the operating threshold; a large threshold score leads to a small FMR

at the expense of a high FNMR.

Figure 5.1 Original image with its minutiae

35

Figure 5.2 Similarity between two fingerprints

36

Figure 5.3 Relations between FMR and FNMR

37

Chapter 6

Conclusion

38

Conclusion

The above implementation was an effort to understand how Biometric Fingerprint

Recognition is used as a form of biometric to recognize identities of human beings. It

includes all the stages from minutiae extraction from fingerprints to minutiae matching

which generates a match score. Various standard techniques are used in the intermediate

stages of processing. This paper presents a fingerprint recognition system, which

measures the similarity between two fingerprints. For this purpose, several fingerprints

are compared. The algorithm presented in this paper, tests all the images without any

fine-tuning for the database.

The limitation of this system is that the poor quality image results in spurious and missed

minutiae, thus the degrading performance of overall system.

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

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

correct location of minutiae. Among them, poor image quality is the most serious one. In

this project, we have combined many methods to build a minutiae extractor and a

minutiae matcher. The following concepts have been used- segmentation using

Morphological operations, minutiae marking by specially considering the triple

branch counting, minutiae 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.

39

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