Project Report

On

“ FAST MULTIMODAL BIOMETRIC APPROACH USING DYNAMIC FINGERPRINT

AUTHENTICATION AND ENHANCED IRIS FEATURES”

Submitted for partial fulfillment of the degree of

B.E. (Computer Technology)

By

Anushree SapreApurva Jain

Sanchita BhrieguShruti Sharma

Under the Guidance of

Ms. Ujwalla Gawande

Department of Computer Technology

Yeshwantrao Chavan College of Engineering, Nagpur

Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur

2012-2013

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YESHWANTRAO CHAVAN COLLEGE OF ENGINEERING NAGPUR

(An Autonomous Institution Affiliated to RTMNU)

Department of Computer Technology

(2012-13)

Certificate

This is to certify that the Project Report titled “Fast Multimodal Biometric

Approach Using Dynamic Fingerprint Authentication and Enhanced Iris

Features “ is submitted towards the partial fulfillment of requirement for

the award of Degree of Bachelor of Engineering in Computer Technology

awarded by Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur.

Submitted by:

Ms Anushree Spare (Roll No: 102)

Ms Apurve Jain (Roll No: 103)

Ms Sanchita Bhriegu (Roll No: 111)

Ms Shruti Sharma (Roll No: 113)

is approved.

Project Guide

Ms. Ujwalla Gawande

Project Coordinator

Mrs. Gauri Chaudhary

Head, Department of Computer Technology

Prof. A.R. Bhagat Patil Date:___________

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Acknowledgements

Success is the manifestation of perseverance, inspiration and motivation. We the

projectees, ascribe our success in this venture to all our lecturers without whom this

project would have been a dream. We would like to thank Prof. Ujwalla Gawande who

guided us throughout the project. It was because of her support and encouragement that

this project materialized.

It was because of the timely guidance of Prof. A.R.Bhagat Patil that has helped us to

complete the project against all odds. We are very graeful to him, for his inspiration,

encouragement and guidance in all phases of our project. We also express our gratiude to

all faculty members for their interminable support ans encougagement. Though words

have their own limitaion we have made a modest effort to acknowledge the support

extended.

We also take opportunity to thank our respected principal Dr. U.P. Waghe for his help in

providing the necessary facilities for completion of project.

Finally, we would like to thank all the members of computer technology department for

their co-operation ans support for timely completion of this project also we would like to

thank our family and friends for their support and encouragement throughout the project.

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Abstract

Unimodal biometric systems have variety of problems such as noisy data, intra-class

variations, restricted degree of freedom, non-universality, spoof attacks, and

unacceptable error rates. Multimodal biometrics refers the combination of two or more

biometric modalities in a single identification system. Biometric identification system

based on the pattern of the human iris and fingerprint are well suited for a high level of

security systems. However, the multimodal biometric system is limited to the time

constraints due to its multiple processing stages. To overcome the problem of time taken

we present a fast multimodal verification system by using the dynamic regions of the

fingerprint image and enhanced iris segmentation method. This paper proposes enhanced

fingerprint and iris recognition system that implements a fusion of both iris and

fingerprint images at score level. The simulations are performed in the MATLAB

environment to evaluate the performance of the implemented algorithms. Results and

observations of the fusion are presented at the end.

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Table of Contents

1.0 Introduction……………………………................................................

.11.1 Introduction

1.2 Finger Print Recognition

1.3 Iris Recognition

1.4Multimodal Biometric System

2.0 Theoretical Background / Literature survey………………………....7

2.1 Literature Survey

3.0 Problem Definition…………………………………………………….12

3.1 Problem Definition

4.0 Architecture and Design………………………………………………14

4.1 Architecture

5.0 Implementation Methodology………………………………………..16

5.1 Database Used

5.2 Fingerprint Matching

5.2.1 Fingerprint Enhancement

5.2.1.1 Normalization

5.2.1.2 Segmentation

5.2.2 Minutiae Extraction

5.2.2.1 Minutia Marking

5.2.2.2 False Minutia Removal

5.3 Iris Recognition

5.3.1 Iris Segmentation

5.3.2 Iris Localization

5.3.3 Iris Normalization

5.3.4 Iris Feature extraction

5.4 Matching and Fusion

6.0 Experimental Results …………..……………………………………27

6.1 Experimental Results

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7.0 Conclusion And Future Work………………………………………33

7.1 Conclusion

7.2 Future Scope

Ennumerative Bibliography……………………………………………35

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List of Figures

Figure No. Figure Name Page No.

1.1 Ridge ending, Bifurcation and short ridge 4

1.2 Structure of Fingerprint 4

1.3 Structure of an Iris 5

4.1 Schematic of score level fusion using hamming distance 15

5.1 Normalization 19

5.2 Segmentation 20

5.3 Termination 21

5.4 Bifurcation 21

5.5 Branch 21

5.6 Minutiae Extraction 21

5.7 False Minutia Removal 23

5.8 Iris Segmentation 24

5.9 Iris Localization 24

5.10 Iris Normalization 25

6.1 Snapshots of GUI 28

6.2 Snapshots of GUI 29

6.3 Snapshots of GUI 29

6.4 Snapshots of GUI 30

6.5 Snapshots of GUI 30

6.6 Snapshots of GUI 31

6.7 Graphical representation of Image acceptance and

rejection

32

List of Tables

Sr. No. Table Page No.

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1 Accuracy Obtained 31

Our Publications

Title of the paper: Fingerprint-Iris Fusion Based Multimodal Biometric System Using

Single Hamming Distance Matcher

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Authors: Prof. UjwallaGawande, AnushreeSapre, Apurva Jain, SanchitaBhriegu, Shruti

Sharma

Name of Journal:International Journal of Engineering Inventions (Online)

Other Details: e-ISSN: 2278-7461, p-ISSN: 2319-6491 Volume 2, Issue 4

(February2013) PP: 54-61

Submittted on: 10-feb-2013

Published on: 15-march-2013

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Chapter 1Introduction

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1.1 Introduction:

Biometrics are technologies used for measuring and analyzing a person's unique

characteristics and is usually associated with the use of unique physiological

characteristics to identify an individual.

The paper is organized as follows. A typical biometric system is discussed ,with the need

of multimodal biometrics is illustrated in Multimodal biometric system and different

fusion techniques are mentioned with the description of score level fusion technique ,

challenges and conclusions are presented in the last section of the paper.

Biometrics is used for maintaining security at different levels.  It is used in measuring

features like face fingerprints, hand geometry, handwriting, iris, retinal, vein, voice etc.

As the need of security is tremendously increasing and as well as transaction, frauds are

increasing, the need for highly secure identification and personal verification technologies

is becoming apparent. Prevailing methods of human identification based on credentials

(identification documents and PIN) are not able to meet the growing demands for

stringent security in applications such as national ID cards, border crossings, government

benefits, and access control. As a result, biometric recognition, or simply biometrics,

which is based on physiological and behavioral characteristics of a person, is being

increasingly adopted and mapped to rapidly growing person identification applications.

Although there are demerits in using biometric system such as noise in sensed data, intra-

class variations, distinctiveness, non-universality, spoof attacks etc.

Above specified limitations imposed by unimodal biometric systems can be overcome by

using multiple biometric modalities. Such systems, known as multibiometric systems, are

expected to be more reliable due to the presence of multiple, fairly independent pieces of

evidence. This approach also enables a user who does not possess a particular biometric

identifier to still enroll and authenticate using other traits, thus eliminating the enrollment

problems and making it universal.

Further, if the biometric trait being sensed or measured is noisy (a fingerprint with a scar

or a voice altered by a cold, for example), the resultant matching score computed by the

matching module may not be reliable. This problem can be solved by installing multiple

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sensors that capture different biometric traits. Such systems, known as multimodal

biometric systems are expected to be more reliable due to the presence of multiple pieces

of evidence. Multimodal systems also provide anti-spoofing measures by making it

difficult for an intruder to spoof multiple biometric traits simultaneously.

Multibiometric system performance is reliable because it has multiple information such as

fusion, individual biometric feature extraction .The fusion of biometric is of 3 types

i)Feature level fusion ii)Score level fusion iii)Decision level fusion. In our paper we have

used score level fusion technique to combine the fingerprint and iris samples.

Challenges to Multi-Biometric System

Followings are the challenges in designing the multi modal system:

1. The information obtained from different biometric sources can be combined at

different levels therefore selecting the best level of fusion will have the direct impact on

performance and cost involved In developing a system.

2. There are Numbers of techniques available for fusion in multi-biometric system and the

multiple source of information is available. Hence it is challenging to find the optimal

solution for the application provided.

3. In multi-biometric systems the information acquired from different sources can be

processed either in sequence or parallel. Hence it is challenging to decide about the

processing architecture to be employed in designing the multi-biometric system.

1.2 Finger Print Recognition:

Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a

match between two human fingerprints. Fingerprints are one of many forms of biometrics used

to identify individuals and verify their identity.The analysis of fingerprints for matching purposes

generally requires the comparison of several features of the print pattern. These include patterns,

which are aggregate characteristics of ridges, and minutia points, which are unique features found

within the patterns.

The major Minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or

dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a

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single ridge splits into two ridges. Minutiae and patterns are very important in the analysis of

fingerprints since no two fingers have been shown to be identical.

Figure 1.1 Ridge ending, Bifurcation and short ridge(dot)

Figure 1.2 : Structure of fingerprint

One of the advantages of using fingerprint as one of the modalities is that fairly small

storage space is requires for the biometric template, reducing the size of the database

required. It is one of the most developed biometrics, with more history, research, and

design. Each and every fingerprint including all the fingers are unique, even identical

twins have different fingerprints. Sound potential for forensic use as most of the countries

have existing fingerprint databases. Relatively inexpensive and offers high levels of

accuracy.

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1.3 Iris Recognition:

Iris Recognition is the process of recognizing a person by analyzing the random patterns

of the iris. The automated method of iris recognition is relatively young existing in patent

since 1994 only.

The iris is the muscle in the eye that regulates the size of the pupil, controlling the amount

of eye that enters the eye. It is the colored portion of the eye with the coloring amount

based on the melatonin pigment present in the muscle. Although the coloration and

structure of iris is genetically linked, the details of the pattern are not. The individual

irises are unique and structurally different, which allows for it to be used for the

recognition purposes.

Figure 1.3: Structure of Iris

1.4 Multimodal Biometric System:

Multimodal biometric systems are those that utilize more than one physiological or

behavioral characteristic for enrollment, verification, or identification. In applications

such as border entry/exit, access control, civil identification, and network security, multi-

modal biometric systems are looked to as a means of reducing false non-match and false

match rates, providing a secondary means of enrollment, verification, and identification if

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sufficient data cannot be acquired from a given biometric sample, and combating attempts

to fool biometric systems through fraudulent data sources such as fake fingers.

The levels of fusion for multimodal systems are broadly categorized into three system

architectures

Fusion at the Feature Extraction Level

Fusion at the Matching Score Level

Fusion at the Decision Level

In Fusion at the Feature Extraction Level, information extracted from the different

sensors is encoded into a joint feature vector, which is then compared to an enrollment

template (which itself is a joint feature vector stored in a database) and assigned a

matching score as in a single biometric system.

In Fusion at the Matching Score Level, feature vectors are created independently for each

sensor and are then compared to the enrollment templates which are stored separately for

each biometric trait. Based on the proximity of feature vector and template, each

subsystem computes its own matching score. These individual scores are finally

combined into a total score which is passed to the decision module.

In Fusion at the Decision Level, a separate authentication decision is made for each

biometric trait. These decisions are then combined into a final vote. This architecture is

rather loosely coupled system architecture, with each subsystem performing like a single

biometric system.

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Chapter 2:

Theoretical Background/Literature Survey

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2.1 Literature Survey

A variety of articles can be found, which propose different approaches for unimodal and

multimodal biometric systems. Many researchers have demonstrated that the fusion

process is effective, because fused scores provide much better discrimination than

individual scores. Such results have been achieved using a variety of fusion techniques.

Following papers have been taken for reference:

Mohamad Abdolahi, Majid Mohamadi, Mehdi Jafari (International Journal of Soft

Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January

2013) implemented a novel multimodal biometric system using fingerprint and iris with

fuzzy logic. In this paper, they have saved fingerprint feature vectors in the form of 128

bit codes which consisted of 68 bit each of terminations and bifurcations. Instead of

saving both the terminations and bifurcations, we are going to save the terminations(X

and Y positions) in a 60 bit feature vector. The reason for not saving the bifurcation is

that, in some of our images, there are no bifurcations at all or very few bifurcation points

available. For extracting iris codes, in this paper, they have first obtained a rectangular

region of the iris image with iris visible in the center. Then, the center pixel is obtained by

dividing the row and column. Certainly the pixel is in the pupil region and its clear pupil

is the darker part in eye, so it can move right to the pixel with a high amount of difference

intensity and mark it, move left to the pixel with a high amount of difference intensity and

mark it and find the center of these points.Same method is applied to find top and bottom

and center of them. Now with these center and peripheral acquired points we can find the

real pupil center with center point and maximum distance drawing a pupil circle

performing the same task to find the iris region and extract iris from eye image. With

Gabor filter features and iris code is extracted. We are going to use canny edge detection

method for segmentation and Daugmain’s rubber sheet model for normalization. A fuzzy

logic method is used for fusion which is given better performance and accuracy.Fuzzy

logic is a kind of soft computing, which mimics human decision making. In our

implementation, we are going to use a single hamming distance matcher to compute the

final result.

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A fast multimodal biometric system using fingerprint and iris fusion is proposed by A.

Jameer Basha1, V. Palanisamy2, and T. Purusothaman3 (IEEE 2010). In this system, they

have used reference point location and minutiae matching for fingerprint recognition and

Daugman’s rubber sheet model for iris recognition. For fusion, effective adaptive rank

level fusion scheme that combine information presented by multiple domain experts

based on the rank-level fusion integration method is employed. The ranks of individual

matchers are combined using the highest rank approach.

Feten BESBES proposed a fusion based multimodal biometric system based on a couple

of modalities recognition: fingerprint and iris, and every part provides its own decision.

The final decision of the system will take in consideration both of the last decisions using

the operator "AND".

Li Xiuyan1, Miao Changyun1, Liu Tiegen2, Yuan Chenhu3 (IEEE 2011) presented a

theoretical analysis and experimental study on multimodal biometric. The theory and

experiments of multimodal biometric were studied based on hand vein, iris and

fingerprint. Simple Average and Weighting Average fusion algorithm, the classical

information fusion methods, were analyzed and the constraint conditions for improving

the recognition accuracy had been deduced. Biometric recognition experiments were

performed finally to verify the theory deduction results. It is significant to future research

on multimodal biometric and provides basis for developing multibiometric systems.

George Chellin Chandran, Dr. Rajesh. R.S (IJCSNS 2009) proposed Performance

Analysis of Multimodal Biometric System Authentication that adopted multiple biometric

traits of an individual, to establish the identity. The system employed multiple sensors to

acquire data pertaining to fingerprint and iris. The independence of the traits ensures the

improvement in performance. The main purpose of the proposed system was to reduce the

error rate as low as possible and improve the performance of the system by achieving

good acceptable rate during identification and authentication.

Adem Alpaslan ALTUN proposed Recognition of Selected Fingerprints and Iris

FeaturesEnhanced by Curvelet Transform with Artificial Neural Networks In this study,

curvelet transform is applied biometric images for enhancement. Obtained results after

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applied curvelet transform is compared to the other traditional image enhancement

algorithms. Features obtained from enhanced fingerprints and iris images are selected by

using Genetic Algorithms because of too huge dataset. Selected features are input to

Artificial Neural Networks for biometric recognition. Thus, the recognition is achieved

very fast without to reduce the performance.

Teddy Ko (IEEE Computer Society 2005) researched on multimodal biometrics and came

up with Multimodal Biometric Identification for Large User Population Using

Fingerprint, Face and Iris Recognition that discusses the various scenarios that are

possible in multimodal biometric systems using fingerprint, face and iris recognition.

Here they have applied various levels of fusion techniques in order to generate output.

KAZI M.M, RODE Y.S (Advances in Computational Research ISSN: 0975-3273 & E-

ISSN: 0975-9085, Volume 4, Issue 1, 2012) proposed a multimodal biometric system

using face and signature in which they used score level fusion. In this paper, matcher

scores are simply added, with no prior normalization. Scores are neither rescaled, nor

weighted to account for differences in matcher accuracy. Whereas in our implementation

of fusion technique, first the individual score is calculated and the average of the two is

considered as final score. If the score obtained is greater than a predefined threshold, then

the output is accepted otherwise it is rejected.

“Fingerprint Recognitionusing Image Segmentation” proposed by SangramBana, Dr.

DavinderKaur((IJAEST) INTERNATIONAL JOURNAL OF ADVANCED

ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 5, Issue No. 1, 012 –

023) is a study and implementation of a fingerprint recognition system based on Minutiae

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

Their approach mainly involves extraction of minutiae points from the sample fingerprint

images and then performing fingerprint matching based on the number of minutiae

pairings among two fingerprints in question. Here, they used a matching algorithm that

was not upto the mark and gave reasonable results. One of the reasons for poor

verification result was the bad quality of fingerprint images and inefficient matching

algorithm as it is vulnerable to effects like scaling and elastic deformations.

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To overcome the problems faced by individual traits, a novel combination is proposed for

the recognition system. Our system improves the accuracy. The main drawback of

multimodal system is that they are time comsuming our system aims at reducing the

processing and making system fast using enhanced iris featurs and dynamic regions of

fingerprint. The system has become fast because we have used single hamming distance

matcher for both fingerprint features and iris features.

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Chapter 3:

Problem Definition

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3.1 Problem Definition

The proposed project on multimodal biometric systems with fingerprint and iris

recognition seeks to alleviate some of the problems of unimodal biometrics by providing

multiple pieces of evidence of the same identity.

Conventional multimodal biometric identification systems tend to have larger memory

footprint, slower processing speeds and a higher implementation and operational cost

This project discusses the various scenarios that are possible to provide smaller memory

footprint and to the improve the performance of multimodal biometric systems using the

combined characteristics iris and fingerprint, the level of fusion (multimodal fusion) is

applied to that are possible and the integration strategies that can be adopted in order to

increase the overall system performance. In order to ensure that the performance of

multibiometric systems such as fingerprint and iris will be powerful with respect to the

quality of obtained fingerprint and iris images, these images are denoised and enhanced.

Finally, the extracted feature vectors of both fingerprint and iris are fused together and

Hamming distance is applied to identify the subject.

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Chapter 4:

Architecture and Design

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4.1 Architecture

Figure 4.1 schematic of score level fusion using hamming distance

.

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Chapter 5:

Implementation Methodology

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5.1 Database Used

In order to check the validity of our implementation, we have used the database provided

to us by our guide Prof. Ujwalla Gawande. Image Database consists of 50 subjects with

four samples of fingerprint and iris each. Good quality images were taken.

5.2 Fingerprint Matching

There are two major for steps performed in order to match two fingerprint templates.

These are as follows:

5.2.1 Fingerprint Enhancement

The performance of minutiae extraction algorithms and other fingerprint recognition

techniques relies heavily on the quality of the input fingerprint images. In an ideal

fingerprint image, ridges and valleys alternate and flow in a locally constant direction.

However the fingerprint images obtained are usually poor due to elements that corrode

the clarity of the ridge elements. This leads to problems in minutiae extraction. Thus,

image enhancement techniques are necessary to reduce the noise and enhance the

definition of ridges against valleys. In order to ensure good performance of the ridge and

minutiae extraction algorithms in poor quality fingerprint images, an enhancement

algorithm to improve the clarity of the ridge structure is necessary.

The pre-processing steps include:

5.2.1.1 Normalization:

Normalization is done so that the gray level values lies within a given set of values. The

fingerprint image is normalized to have a predefined mean and variance. This is required

as the image usually has distorted levels of gray values among the ridges and the valleys.

Normalization allows standardizing the distorted levels of variation in the gray scale

values. Normalization involves pixel-wise operations and does not change the ridge and

valley structures.

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Normalization is a linear process. Suppose the intensity range of the image is 50 to 180

and the desired range is 0 to 255 the process entails subtracting 50 from each of pixel

intensity, making the range 0 to 130. Each pixel intensity is multiplied by 255/130,

making the range 0 to 255.

The normalized image is given by

N (i, j) = M0 + √V0 (I (i, j) - M) 2/V if I (i, j) > M

M0 - √V0 (I (i, j) - M) 2/V otherwise

Where for a pixel I (i, j) the estimated mean and variances are M and V respectively. M0

and V0 denote the desired mean and variance values.

Histogram equalization, as normalization method, is a process to enhance the contrast of

images by transforming its intensity values. Usually a fingerprint image has different gray

values for every pixel. It is desirable to have the gray value around a mean value. This is

achieved by histogram equalization. It increases the local contrast of images. Thus the

intensities can be distributed on the histogram. This allows for areas of lower local

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

equalization accomplishes this by effectively spreading out the intensity values.

The histogram of the original image illustrates that all the intensity values lie on the right

hand side of the 0–255 scale, with no pixels in the left hand side. The histogram of the

normalized image shows that the range of intensity values has been adjusted such that

there is a more balanced distribution between the dark and light pixels. Normalizing the

image improves the contrast between the ridges and valleys. It does not alter the shape of

the original histogram plot. The relative position of the values along the x axis is shifted.

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Figure5.1 Normalization

5.2.1.2 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 are confusing with those spurious minutia that

are generated when the ridges are out of the sensor.

Estimate the block direction for each block of the fingerprint image with WxW in size(W

is 16 pixels by default). The algorithm is:

i) Calculate the gradient values along x-direction (gx) and y-direction (gy) for each

pixel of the block. Two Sobel filters are used to fulfill the task.

ii) For each block, use Following formula to get the Least Square approximation of

the block direction.

tg2ß = 2 (gx*gy)/(gx2-gy2) for all the pixels in each block.

The formula is easyto 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.

tg2= 2sin cos /(cos2 -sin2 )

iii) After finished with the estimation of each block direction, those blocks without

significant information on ridges and furrows are discarded based on the

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following formulas: E={2(gx*gy)

+(gx2gy2)}/W*W*(gx2+gy2)

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

regarded as a background block.

Figure 2.2 : Segmentation

5.2.2 Minutiae Extraction

First step in minutiae marking is fingerprint thinning abd binarization. Thinning is done to

eliminate the redundant pixels till the ridge is just one-pixel wide. A builtin MATLAB

morphological thinning function is used here. Binarization is converting the image in the

form of 0s and 1s.

5.2.2.1 Minutia Marking

After the fingerprint ridge thinning, marking minutia points is relatively easy. But it is

still not a trivial task as most literatures declared because at least one special case evokes

my caution during the minutia marking stage.

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.If the central pixel is 1 and has only 1

one-value neighbor, then the central pixel is a ridge ending.

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Figure 5.3: Bifurcation Figure 5.4: Termination Figure 5.5: Branch

Figure 5.5 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

neighbor 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.

Where Pi is the pixel value in the neighborhood of P

Figure 5.6: Minutia Extraction

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5.2.2.2 False Minutiae Removal

The preprocessing stage does not totally heal the fingerprint image. For example, false

ridge breaks due to insufficient amount of ink and ridge cross-connections due to over

inking are not totally eliminated. Actually all the earlier stages themselves occasionally

introduce some artifacts which later lead to spurious minutia. These false minutia will

significantly affect the accuracy of matching if they are simply regarded as genuine

minutia. So some mechanisms of removing false minutia are essential to keep the

fingerprint verification system effective.

Following steps are used in order to remove false minutia:

i) If the distance between one bifurcation and one termination is less than D and the two

minutiae are in the same ridge. Remove both of them. Where D is the average inter-ridge

width representing the average distance between two parallel neighboring ridges.

ii) If the distance between two bifurcations is less than D and they are in the same ridge,

remove the two bifurcations.

iii) If two terminations are within a distance D and their directions are coincident with a

small angle variation. And they suffice the condition that no any other termination is

located between the two terminations. Then the two terminations are regarded as false

minutia derived from a broken ridge and are removed.

iv) If two terminations are located in a short ridge with length less than D, remove the

two terminations.

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Figure5.7: False Minutiae Removal

5.3 Iris Recognition

Following steps are applied in order to extract iris feature vectors for matching:

5.3.1 Iris Segmentation

The first stage of iris recognition is to isolate the actual iris region in a digital eye image.

The iris region, can be approximated by two circles, one for the iris/sclera boundary and

another, interior to the first, for the iris/pupil boundary. The eyelids and eyelashes

normally occlude the upper and lower parts of the iris region. Also, specular reflections

can occur within the iris region corrupting the iris pattern. A technique is required to

isolate and exclude these artefacts as well as locating the circular iris region.

It was decided to use circular Hough transform for detecting the iris and pupil boundaries.

This involves first employing Canny edge detection to generate an edge map. Gradients

were biased in the vertical direction for the outer iris/sclera boundary.

Eyelids were isolated by first fitting a line to the upper and lower eyelid using the linear

Hough transform. A second horizontal line is then drawn, which intersects with the first

line at the iris edge that is closest to the pupil.The second horizontal line allows maximum

isolation of eyelid regions. Canny edge detection is used to create an edge map, and only

horizontal gradient information is taken. The linear Hough transform is implemented

using the MATLAB® Radon transform, which is a form of the Hough transform. If the

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maximum in Hough space is lower than a set threshold, then no line is fitted, since this

corresponds to non-occluding eyelids. Also, the lines are restricted to lie exterior to the

pupil region, and interior to the iris region. A linear Hough transform has the advantage

over its parabolic version, in that there are less parameters to deduce, making the process

less computationally demanding.

Figure 5.8: Iris Segmentation

5.3.2 Iris Localization

Detecting and removing the occluding eyelashes is done by iris localization using circular

hough transform algorithm.

Figure 5.9: Iris Localization

5.3.3 Iris Normalization

After successfully extracting the iris part from the eye image, in order to allow

comparisons between different irises, transform the extracted iris region so that it has a

fixed dimension, and hence removing the dimensional inconsistencies between eye

images due to the stretching of the iris caused by the pupil dilation from varying levels of

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illumination. Therefore, this normalization process will produce irises with same fixed

dimensions so that two photographs for the same iris under different lighting conditions

will have the same characteristic features.

For normalisation of iris regions a technique based on Daugman’s rubber sheet model

was employed. The centre of the pupil was considered as the reference point, and radial

vectors pass through the iris region. A number of data points are selected along each

radial line and this is defined as the radial resolution. The number of radial lines going

around the iris region is defined as the angular resolution. Since the pupil can be non-

concentric to the iris, a remapping formula is needed to rescale points depending on the

angle around the circle.

Figure 5.10: Iris Normalization

5.3.4 Iris Feature Extraction

This is the most key component of an iris recognition system and determines the system’s

performance to a large extent. Iris recognition produces the correct result by extracting

features of the input images and matching these features with known patterns in the

feature database. Features are the attributes or values extracted to get the unique

characteristics from the image. Features from the iris image are extracted using Haar

Wavelet decomposition process. In the wavelet decomposition the image is decomposed

into four coefficient i.e., horizontal, diagonal, vertical and approximation. The

approximation coefficients are further decomposed into four coefficients. The sequences

of steps are repeated for five levels and the last level coefficients are combined to form a

vector. The combined vector is binarized to allow easy comparisons between the iris

codes for database and query image.

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The binarized feature vectors are passed to the matching module to allow comparisons.

5.4 Matching and Fusion

The comparison is done between iris codes and fingerprint codes generated for database

and query images using hamming distance approach. In this approach the difference

between the bits of two codes of both are counted and the number is divided by the total

number of comparisons.

Where A is the binary vector for database image and B is the binary vector for query

image while N is the number of elements. This matching score (MS) is used as input for

the fusion module where the final matching score is generated.

MS= 1N∑i=1

N

A iBi

No individual trait can provide 100% accuracy. Thus to overcome the problems faced by

individual traits, a novel combination is proposed for the recognition system. The

integrated system also provide anti spoofing measures by making it difficult for an

intruder to spoof multiple biometric traits simultaneously. Scores generated from

individual traits are combined at matching score level by calculating the average of the

score obtained from individual modalities.

A predefined threshold is set based on the overall scores obtained. If the combined and

averaged score is greater than the threshold then the person is genuine otherwise the

person is considered as an imposter. In our implementation, we set the threshold to 70%.

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Chapter 6:

Experimental Results

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6.1 Experimental Results

Final results are obtained by applying single hamming distance matcher individually for each feature vector, and then the scores obtained are fused by averaging features of both fingerprint and iris.

Figure 6.1 : Selection of fingerprint query image of subject 7

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Figure 6.2: Accurate matching of fingerprint and iris image.

Figure 6.3: Result showing Person is genuine

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Figure 6.4: Selection of fingerprint query image of subject 18

Figure 6.5: Inaccurate matching of fingerprint and iris image.

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Figure 6.6: Result showing person is imposter due to incorrect matching

False Rejection Rate (FRR): For an image database, each query image is matched

against the database images of the same finger to compute the False Rejection Rate. Same

procedure is repeated for iris database.

False Acceptance Rate (FAR): Also the query image of each finger in the database is

matched against the first sample of the remaining fingers to compute the False

Acceptance Rate. Same procedure is repeated for iris database.

In our implementation, the FAR and FRR was 35-40% approximately and the overall

accuracy in the verification stage was about 70-75%.

Fingerprint Iris Fingerprint+Iris

Correct Match 37 39 37

Incorrect Match 13 11 13

Accuracy Rate 0.74 0.78 0.74

Table 1: Accuracy Obtained

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The graph below shows the acceptance and rejection of images based on their scores. On

the X-axis are the images and on the Y-axis are the final matching scores.

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 490

10

20

30

40

50

60

70

80

Image Vs. Matching Score

Series1

Figure 6.7: Graphical representation of Image acceptance and rejection

We also measured the time elapsed when using individual matchers and the fusion of the

scores obtained using the MATLAB function tic; any statements; toc; the results

obtained are as follows-

Fingerprint matcher - elapsed time is 0.77715 seconds

Iris matcher - elapsed time is 0.08923 seconds

Fusion - elapsed time is 0.026365 seconds

Thus we see that fusion takes minimum time to calculate the final output.

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Chapter 7:

Conclusion and Future Scope

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7.1 Conclusion

Thus, our multimodal system that uses dynamic fingerprint authentication and enhanced

iris features provides faster and improved results because of the use of single hamming

distance matcher used in the matching stage at the time of fusion. We observed that in

order to extract correct feature vectors to be used for matching, the preprocessing steps

must be applied accurately so that they remove the noise present in the image obtained at

the image acquisition phase. Also, we felt that although hamming distance increases the

computation speedup of our system, it is not the most accurate measure for matching.

Various other distance matchers can also be used instead.

7.2 Future Scope

While our implementation is successfully able to decide whether the person is genuine or

an imposter, it is by no means perfect. As shown by the results, there are erroneous results

produced sometimes.

In order to make our implementation more efficient, there is scope in the matching and

fusion module of our system. After extracting the correct feature vectors from both the

modalities, it is essential to use correct matching and fusion algorithm to decide the

genuineness of a person. During the implementation period we, felt that instead of

hamming distance we could have obtained better results if we would have used other

distance matchers for example Euclidean distance.

Another improvement would be to encrypt the feature vectors of both the modalities

obtained before fusion using some encryption algorithms in order to provide heightened

security.

Lastly, our system works for only images that are of good quality. For poor quality

images, we need to modify our code.

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