SHARMA THESIS1

M.E.DIGITAL COMMUNICATION
PIET Page i
ACKNOWLEDGEMENT
I, hereby, take an opportunity to convey my gratitude for the generous
assistance and cooperation, that I received from the PG Director Dr. P.H Tandel and
to all those who helped me directly and indirectly.
I am sincerely thankful to my Guide, Asst .Prof. Mitul M. Patel whose
constant help, stimulating suggestions and encouragement helped me in completing
my Literature Review work successfully.
I am also thankful to Prof. M.A.Lokhandwala, Head of the Department and
other faculty members who have directly or indirectly helped me whenever it was
required by me.
I am thankful to Prof. Jitendra Chaudhary and Prof. Sankar Parmar who
helped me and encouraged me in my work.
Finally, I am also indebted to God and my friends without whose help I would
have had a hard time managing everything on my own.
Sharma Ashok Sukhbir
[110370722003]

PIET Page ii
TABLE OF CONTENTS
ACKNOWLEDGEMENT.............................................................................................i
TABLE OF CONTENTS .............................................................................................ii
LIST OF FIGURE .......................................................................................................iv
LIST OF TABLES.......................................................................................................vi
ABSTRACT vii
CHAPTER 1 INTRODUCTION ....................................................................................1
1.1 OVERVIEW OF BIOMETRICS.....................................................................2
1.2 PALM PRINT BIOMETRIC RECOGNITION...............................................4
1.3 HISTORY ........................................................................................................5
1.4 DESCRIPTION AND OUTLINE OF THE THESIS ......................................7
CHAPTER 2 BIOMETRICS IN AUTHENTICATION .................................................9
2.1 INTRODUCTION .........................................................................................10
2.2 PROPERTIES OF BIOMETRICS.................................................................10
2.3 BIOMETRIC SYSTEM BLOCK DIAGRAM ..............................................11
2.4 VERIFICATION AND IDENTIFICATION.................................................12
2.5 LEADING BIOMETRIC TECHNOLOGIES ...............................................13
2.5.1 FINGER-SCAN..................................................................................13
2.5.2 FACIAL-SCAN..................................................................................14
2.5.3 IRIS-SCAN.........................................................................................14
2.5.4 VOICE-SCAN ....................................................................................15
2.6 DESIRED FEATURES IN A BIOMETRIC .................................................16
CHAPTER 3 EXISTING PALMPRINT RECOGNITION ALGORITHMS................18
CHAPTER 4 PROPOSED SYSTEM............................................................................22

PIET Page iii
4.1 ROI EXTRACTION ......................................................................................24
4.1.1 BINARIZATION..............................................................................24
4.1.2 CONTOURING ................................................................................25
4.1.3 SELECTING REFERANCE POINT................................................26
4.1.4 CROPING ROI.................................................................................27
4.2 ENHANCEMENT OF ROI...........................................................................27
4.3 FEATURE EXTRACTION AND CODING.................................................29
4.4 ZERNIKE MOMENTS .................................................................................31
CHAPTER 5 RESULTS ...............................................................................................35
CHAPTER 6 CONCLUSION AND FUTURE SCOPE................................................44
6.1 CONCLUSION..............................................................................................45
6.2 FUTURE SCOPE...........................................................................................45
REFERENCES .............................................................................................................46
APPENDIX……………………………………………………………………….....50
APPENDIX-A..............................................................................................................51

PIET Page iv
LIST OF FIGURE
Figure 1.1:Biometric characteristics ..............................................................................3
Figure 1.2:Typical scheme of a biometric system. ........................................................4
Figure 2.1:Block Diagram of the Proposed Algorithm................................................11
Figure 3.1:Schematic Diagram of Palmprint Acquisition System [7] .........................20
Figure 3.2:Pegs and the Cropped Area of the Palm [7] ...............................................20
Figure 4.1:Block diagram of palm print verification system.......................................23
Figure 4.2:Database examples .....................................................................................24
Figure 4.3:Binarized image..........................................................................................25
Figure 4.4:Palm print contour......................................................................................26
Figure 4.5:Distance transform of contour image .........................................................26
Figure 4.6:Region to be cropped..................................................................................27
Figure 4.7:ROI.............................................................................................................27
Figure 4.8:(a) palm print ROI (b) coarse reflection, (c) uniform brightness palm print
image, (d) Enhanced palm print image........................................................................28
Figure 4.9:Principle Lines and Wrinkles in a Palm [20] .............................................30
Figure 4.10:Three Sets of Palmprint Images with Similar Principal Lines from
Different People...........................................................................................................31
Figure 4.11:Square to circular transform.....................................................................33
Figure 5.1:Minimum distance for test image 8............................................................36
Figure 5.6:Minimum distance for test image 11..........................................................38
Figure 5.7:Value of minimum distance for test image 20 ...........................................39
Figure 5.8:Train index values for corresponding test images......................................39
Figure 5.9:Minimum distance graph for all test images ..............................................40
Figure 5.10:False matched images...............................................................................40
Figure 5.12:Result of thresholding ..............................................................................41
Figure 5.13:Smallest distance histogram .....................................................................42

PIET Page v
Figure 5.14:Second smallest distance histogram.........................................................42
Figure 5.15:Reliability of identification ......................................................................43

PIET Page vi
LIST OF TABLES
Table 4.1: DPI REQUIREMENTS.......................................................................................30
Table 5.1: EFFICIENCY.......................................................................................................43

PIET Page vii
ABSTRACT
Biometrics is identification of an individual on the basis of unique physiological and
behavioral patterns. Biometrics is fast replacing other means of authentication like
passwords and keys due to the inherent drawbacks in them and increased
effectiveness and reliability of the biometric modalities. The passwords can be
forgotten or hacked, while keys can be lost. The individual’s unique physiological or
behavioral characteristics, on the other hand, are hard to forged or lost. Finger print
and face are the common biometrics used nowadays, but they have inherent problems.
The illumination variations affect the performance of face recognition algorithms,
while finger print, along with technological challenges, has less user acceptability due
to the historical use in crime investigations. Palm print is a biometric modality which
has recently drawn great attention owing to its strengths like ease of acquisition,
robustness, user acceptance in addition to its uniqueness and rich distinguishable
contents and features. Palm print biometric is potentially a very effective biometrics
in sense it offers widely discernible and discriminating features like palm lines,
wrinkles, minutiae and delta points.
Limited work has been reported on palm print based identification/verification despite
of its significant features. Efforts have been made to build a palm print based
recognition system based on structural features of palm print like crease points, line
features, Datum points, local binary pattern histograms. There also exists systems
based on statistical features of palm print extracted using Fourier transforms, Discrete
Cosine Transforms, Karhunen-Lowe transforms, Wavelet transforms, Independent
Component Analysis, Gabor filter, Linear Discriminant Analysis(LDA), Neural
networks, statistical signature and hand geometry.
In current work, the palm print identification is done with the help of a statistical
method, Moments. Moments are used from early times in image processing for
character recognition and statistical analysis. Using this technique in biometrics
induces some benefits like invariance in rotation and translation. The moments are
used as features and its extraction and matching will be done using the software
MATLAB.

PIET Page 1
CHAPTER 1
INTRODUCTION

PIET Page 2
1.1 OVERVIEW OF BIOMETRICS
Studies of physical and behavioral traits are known as biometrics. Being specific of an
individual, they guarantee one’s identity in security control situations. A well-known
example of a biometric characteristic are fingerprints, which is the most widely used.
It is theoretically impossible to find any two individuals with the same fingerprint.
This is a crucial property of a biometric characteristic: to be unique for each person.
Other equally important aspects regarding biometric characteristics are universality,
as they have to be present in all individuals; and permanence, so they are constant
during one’s life. Moreover, they should be easy to extract. At present time, there is
research activity in a broad range of biometric characteristics which can be divided
into physical and behavioral. Physical are, for instance, fingerprints, iris, retinal
capillary structure, face, and hand recognition. Examples of behavioral traits are voice
and handwriting. Figure 1illustrates several biometric characteristics.
Biometric systems can be used for identification and recognition purposes. In all cases
there should be a database where biometric features from a set of individuals are
stored. The role of the system is to compare an input with all the entries in the
database and verify if there is a match, thus confirming the identity of the individual.
To compare any kind of biometric characteristics it is necessary to represent them in a
stable fashion. For instance, it is not feasible to directly compare images from two
palm prints, as it is practically impossible to place the hand in the exact same position
in different occasions, producing slightly different images that have to be compared
income way.
This is the most crucial aspect and can be divided into two tasks:
1. Represent a biometric characteristic in reproducible and stable features
that resist input variability.
2. Compare such features so users can accurately be identified.
These two questions are in the core of a biometric system and are addressed by most
of the researching the field. Its importance is highlighted in figure 2 where the layout
of a biometric system is depicted.

PIET Page 3
Figure Error! No text of specified style in document..1:Biometric characteristics
a) Retinal fundus image and b) detection of correspondent vascularization. c) and d) are examples of
retinal processed images from different persons. e) and f) are fingerprints from twin sisters, with
noticeable differences to the naked eye. g) represents the pressure-time plot of the utterance(complete
unit of speech), from which spectral information can be retrieved to identify a speaker. h) and i) depict
the illumination system for acquisition of 3D palm print information, resulting in j). k) and l) are iris
from two different persons. m) depicts palm veins detections using infra-red lighting.

PIET Page 4
Figure 1.2:Typical scheme of a biometric system.
1.2 PALM PRINT BIOMETRIC RECOGNITION
During the last years there has been an increasing use of automatic personal
recognition systems. Palm print based biometric approaches have been intensively
developed over the last 12 years because they possess several advantages over other
systems.
Palm print images can be acquired with low resolution cameras and scanners and still
have enough information to achieve good recognition rates. If high resolution images
are captured, ridges and wrinkles can be detected. Forensic applications typically
require high resolution imaging, with at least 500 dpi.
According to the classification in, palm prints are one of the four biometric modalities
possessing all of the following properties:
• Universality, which means, the characteristic should be present in all
individuals;
• Uniqueness, as the characteristic has to be unique to each individual;
• Permanence: its resistance to aging;
• Measurability: how easy is to acquire image or signal from the individual;
Acquisition
Feature
Extraction
Database
Feature
Matching
User input
User registration
System
output
Verification
Identification

PIET Page 5
• Performance: how good it is at recognizing and identifying individuals;
• Acceptability: the population must be willing to provide the characteristic;
• Circumvention: how easily can it be forged;
The other three modalities are fingerprints, hand vein and ear canal. For instance, iris
based methods, which are the most reliable; require more expensive acquisition
systems than palm print systems. Face and voice characteristics are easier to acquire
than palm prints, but they are not so reliable. Overall, palm print based systems are
well balanced in terms of cost and performance.
1.3 HISTORY
In many instances throughout history, examination of handprints was the only method
of distinguishing one illiterate person from another since they could not write their
own names. Accordingly, the hand impressions of those who could not record a name
but could press an inked hand onto the back of a contract become an acceptable form
of identification. In 1858, Sir William Herschel, working for the civil service of India,
recorded a handprint of back of a contract for each worker to distinguish employees
from others who might claim to be employees when payday arrived. This was the first
recorded systematic capture of hand and finger images that were uniformly taken for
identification purposes.
The first known AFIS system built to support palm print is believed to have been built
by a Hungarian company. In late 1994, latent experts from the United States
benchmarked the palm system and invited the Hungarian company to the 1995
International Association for Identification (IAI) conference. The palm system was
subsequently bought by a US company in 1997.
In 2004, Connecticut, Rhode Island and California established state wide palm print
databases that allowed law enforcement agencies in each state to submit unidentified
latent palm prints to be searched against each other’s database of known offenders.
Australia currently houses the largest repository of palm prints in the world. The new
Australian Nation Automated Fingerprint Identification system (NAFIS) includes 4.8

PIET Page 6
million palm prints. The new NAFIS complies with the ANSI/NIST international
standard for fingerprint data exchange, making it easy for Australian police services
to provide finger print records to overseas police forces such as Interpol or the FBI,
when necessary.
Over the past several years, most commercial companies that provide fingerprint
capabilities have added the capability for storing and searching palm print records.
While several state and local agencies within the US have implemented palm systems,
a centralized nation palm system has yet to be developed. Currently, the Federal
Bureau of Investigation (FBI) Criminal Justice Information Services (CJIS) Division
houses the largest collection of criminal history information in the world. This
information primarily utilizes fingerprints as the biometric allowing identification
services to federal, state, and local users through the Integrated Automated
Fingerprint Identification system (IAFIS). The Federal Government has allowed
maturation time for the standards relating to palm data and live-scan capture
equipment prior to adding this capability to the current services offered by the CJIS
Division. The FBI Laboratory Division has evaluated several different commercial
palm AFIS systems to gain a better understanding of the capabilities of various
vendors. Additionally, state and local law enforcement have deployed systems to
compare latent palm prints against their own palm print databases. It is a goal to
leverage those experiences and apply them towards the development of a National
palm Print Search System.
In April 2002, a Staff Paper on palm print technology and IAFIS palm print
capabilities were submitted to the identification Services (IS) Subcommittee, CJIS
Advisory Policy Board (APB). The Joint Working Group then moved “for strong
endorsement of the planning, costing, and development of an integrated latent print
capability for palms at CJIS Division of the FBI. This should proceed as an effort
along the same parallel lines that IAFIS was developed and integrate this into the
CJIS technical capabilities...”
As a result of this endorsement and other changing business needs for law
enforcement, the FBI announced the Next Generation IAFIS (NGI) initiative. A major
component of the NGI initiative is the development of the requirements for and

PIET Page 7
deployment of an integrated NATIONAL Pam print service. Law enforcement
agencies indicate that at least 30 percent of the prints lifted from crime scenes – from
knife hilts, gun grips, steering wheels, and window panes – are of palms, not fingers.
For this reason, capturing and scanning latent palm prints is becoming an area of
increasing interest among the law enforcement community. The improving law
enforcement’s ability to exchange a more complete set of biometric information,
making additional identifications, quickly aiding in solving crimes that formerly may
have not been possible, and improving the overall accuracy of identification through
the IAFIS criminal history records.
1.4 DESCRIPTION AND OUTLINE OF THE THESIS
In this work, a palmprint recognition algorithm based on Zernike moments is
implemented in MATLAB environment. Nevertheless, built-in functions in
MATLAB® Image Processing Toolbox are almost not utilized in order to develop a
platform-independent algorithm. The developed algorithm is first tested on The Hong
Kong Polytechnic University Palmprint Database.
This thesis is organized as follows: In Chapter II, biometrics, the emerging and
reliable authentication method, is discussed in detail. In this chapter, key metrics used
in the evaluation of biometric systems have been defined, and advantages and
disadvantages of some leading biometric technologies currently being used are
mentioned. Moreover, advantages of palmprint as a biometric have been explained.
In Chapter III, brief information is given about The Hong Kong Polytechnic
University Palmprint Database, the most commonly used palmprint database which is
also used in this thesis. Furthermore, some of the palmprint recognition methods in
the literature are described and results obtained in these studies are presented.
In Chapter IV details of the developed palmprint recognition algorithm are given. In
this chapter, the algorithm is divided into three sub-blocks and each sub-block is
detailed along with the discussions related to the effects of different parameters.

PIET Page 8
In Chapter V, results of the developed algorithm on The Hong Kong Polytechnic
University Palmprint Database are presented. Obtained results are investigated from
different side of views and factors affecting results are discussed. Moreover, some
strong and weak points of the algorithm together with some possible improvements on
palmprint recognition system are discussed in Chapter VI.

PIET Page 9
CHAPTER 2
BIOMETRICS IN AUTHENTICATION

PIET Page 10
2.1 INTRODUCTION
Because biometric based authentication is emerging as a powerful method for reliable
authentication, which is of great importance in our lives, biometrics is becoming
increasingly popular. In 2001, the highly respected MIT Technology Review
announced biometrics as one of the “top ten emerging technologies that will change
the world” [1]. Also Rick Norton, the executive director of the International Biometric
Industry Association (IBIA), pointed out the increase in biometric revenues by an
order of magnitude over the recent years. Biometric revenues, which were $20 million
in 1996, increased by 10 times and reached $200 million in 2001. Rick Norton
expects a similar increase in biometric revenues in next 5 years period, from 2001 to
2006, thereby expecting them to reach $2 billion by 2006[1]. Similarly, International
Biometric Group, a biometric consulting and integration company in New York City,
estimate biometric revenues to be around $1.9 billion in 2005[1].
2.2 PROPERTIES OF BIOMETRICS
Researchers noticing the increase in biometric revenues are trying to develop better
algorithms for existing biometrics andor to find new biometrics for authentication.
Whether new or existing, all practical biometrics should possess five properties
described below [2]:
1. Universality: All individuals should possess the biometric characteristics.
2. Uniqueness: The biometric characteristics of different individuals should
not be the same.
3. Permanence: The biometric characteristics of individuals should not change
severely with the time.
4. Collectability: The biometric characteristics should be measurable with
some practical device.
5. Acceptability: Individuals should not have objections to the measuring or
collection of the biometric.

PIET Page 11
2.3 BIOMETRIC SYSTEM BLOCK DIAGRAM
Figure 2.1:Block Diagram of the Proposed Algorithm
After the biometric that is to be utilized is decided, the question how a biometric
system can be implemented naturally arises. Figure 2.1 shows the general block
diagram of a biometric system. As shown in Figure 2.1, biometric systems generally
consist of the following components:
 Data Acquisition Block: This is the block in which biometric data is
captured and is transferred to feature extraction and coding block. The
biometric data may also be compressed in this block, especially when
the data acquisition is performed at a remote location.
 Transmission Channel Block: This is an optional block in the sense
that some biometric systems do not consist of this block. Although
transmission channels are internal to the device in self-contained

PIET Page 12
systems, some biometric systems may be distributed and may have
central data storage and many remote data acquisition points. The
transmission channel for distributed systems might be a local area
network (LAN), a private Intranet, or even the Internet. [1]
 Feature Extraction and Coding Block: This is the block in which
acquired biometric sample is processed. Processing consists of
segmentation, the process of separating relevant biometric data from
background information, and feature extraction, the process of locating
and extracting desired biometric data. After segmentation and feature
extraction, a biometric template, a mathematical representation of the
original biometric, is obtained by encoding extracted features.
 Distance Matching and Decision Policy Block: This is the final block
in a biometric system, where the final decision is made. The biometric
template obtained in feature extraction and coding block is compared
to one or more templates in the data storage by selected matching
algorithm, which determines the degree of similarity between
compared templates. The final decision is usually made based on the
result of the matching algorithm and empirically determined
thresholds.
2.4 VERIFICATION AND IDENTIFICATION
The most important distinction in biometrics is between verification and
identification. Verification systems verify or reject users’ identity. In verification
systems, the user is requested to prove that he/she is the person he/she claims to be.
Therefore; the user should first claim an identity by providing a username or an ID
number. After claiming the identity, the user provides a biometric data to be
compared against his or her enrolled biometric data. The biometric system then
returns one of two possible answers, verified or not verified. Verification is usually
referred to as 1:1 (one-to-one), since the biometric data provided by the user is only
compared against the enrolled biometric data of the person that the user claims to be.
Identification systems, on the other hand, try to identify the person providing the
biometric data. In identification systems, the user is not required to claim an identity;
which is not the case in verification systems, instead he/she is only requested to

PIET Page 13
provide a biometric data. Another difference of identification from verification is that
user’s biometric data is compared against a number of users’ biometric data.
Therefore; identification is generally referred as 1:N (one-to-N or one-to-many). Then
the system returns an identity such as a username or an ID number.
2.5 LEADING BIOMETRIC TECHNOLOGIES
2.5.1 FINGER-SCAN
Finger-scan is a well-known biometric technology which is used to identify and verify
individuals based on the discriminative features on their fingerprints. Many finger-
scan technologies are based on minutiae points, which are irregularities and
discontinuities characterizing fingerprint ridges and valleys. [3]
2.5.1.1 Advantages of Finger-Scan Technology
 It is proven to have very high accuracy.
 It does not require complex user – system interaction; therefore little
user training is enough to ensure correct placement of fingers.
 It provides the opportunity to enroll up to 10 fingers.
2.5.1.2 Disadvantages of Finger-Scan Technology
 High resolution images are required to be acquired due to the small
area of a fingerprint and this results is in more expensive acquisition
devices.
 Small percentage of users; elderly populations, manual laborers and
some Asian populations; are shown to be unable to enroll in some
finger-scan systems according to International Biometric Group’s
Comparative Biometric Testing. [3]
 As mentioned before, some people may tend to wear down their
fingerprints in time because of their physical work.
 Individuals may have objections to collection of their fingerprints
because they may have doubts about usage of their fingerprints for
forensic applications.

PIET Page 14
2.5.2 FACIAL-SCAN
Facial-scan is a biometric technology which is used to identify and verify individuals
based on the discriminative features on their faces. Nonetheless, it is generally used
for identification and surveillance instead of verification. Facial-scan technologies use
some of many discriminative features on face such as eyes, nose, lips etc. [3]
2.5.2.1 Advantages of Facial-Scan Technology
 It is the only biometric which provides the opportunity to identify
individuals at a distance avoiding user discomfort about touching a
device.
 It can use images captured from various devices from standard video
cameras to CCTV cameras.
2.5.2.2 Disadvantages of Facial-Scan Technology
 Changes in lighting conditions, angle of acquisition and background
composition may reduce the system accuracy.
 The face is a reasonably changeable physiological characteristic.
Addition or removal of eyeglasses, changes in beard, moustache,
make-up and hairstyle may also reduce the system accuracy.
 In order to take changes in environmental conditions and user
appearance into account, facial-scan technologies usually store many
templates for each individual and this results in higher memory
requirement for each individual compared to many other biometrics.
 Because face of users may be acquired without their awareness, users
may have objections to facial-scan deployments.
2.5.3 IRIS-SCAN
Iris-scan is a biometric technology which is used to identify and verify individuals
based on the distinctive features on their irises. Iris-scan technologies use the patterns
that constitute the visual component of the iris to discriminate between individuals.[3]

PIET Page 15
2.5.3.1 Advantages of Iris-Scan Technology
 It is proven to have smallest FMR among all biometrics, therefore; iris
is the most suitable biometric for applications requiring highest level of
security.
 Iris does not change in time, therefore; it does not require reenrollment
which other technologies require after a period of time due to changes
in the biometric.
2.5.3.2 Disadvantages of Iris-Scan Technology
 It requires complex user – system interaction, particularly precise
positioning of head and eye. Some systems even require that users do
not move their head during acquisition.
 Very high resolution images are required to be acquired due to the
small area of an iris, therefore; acquisition devices are quite expensive.
 There is a public objection to using an eye-based biometric even
though many people are not aware of the fact that infrared illumination
is used in iris-scan technology. Were they aware, they might be a much
stronger reaction to this technology.
2.5.4 VOICE-SCAN
Voice-scan is a biometric technology which is used to identify and verify individuals
based on the distinctive aspects of their voice. Voice-scan technologies use different
vocal qualities such as fundamental frequency, short-time spectrum of speech and
spectograms (time – frequency – energy patterns).[3]
2.5.4.1 Advantages of Voice-Scan Technology

PIET Page 16
 Various acquisition devices including microphones, land and mobile
phones can be utilized and these devices are relatively cheaper than
acquisition devices used in other biometrics.
 Users are prompted to select a pass phrase during enrollment and they
are asked to repeat the same pass phrase during verification and
identification. The probability that imposters guess the correct pass
phrase adds an inherent resistance against false matching.
2.5.4.2 Disadvantages of Voice-Scan Technology
 Poor reception quality, ambient noise and echoes may degrade the
system accuracy.
 The voice is also a changeable biometric characteristic. Changes in
voice due to illness, lack of sleep and mood may reduce the system
accuracy.
 Voice-scan is subject to possibility of recording and replay attacks.
 Users are requested to repeat the pass phrase a number of times during
enrollment. Therefore, enrollment process in voice-scan is somewhat
longer than that in other biometrics.
 Templates in voice-scan usually occupy a number of times more space
than those in other biometrics.
2.6 DESIRED FEATURES IN A BIOMETRIC
As it is seen, all biometric technologies mentioned above have both advantages and
disadvantages. In other words, there is no perfect biometric technology that has no
disadvantage. However, it is possible to figure out the desired features in a biometric
technology by inspecting advantages and disadvantages of the biometric technologies
above. The list of desired features in a biometric technology is given below:
 High Accuracy
 Zero or very small FTER
 Permanence of biometric in time
 Utilization of cheap acquisition devices

PIET Page 17
 Resistance to changes in environmental conditions
 No or very little public objection (Acceptability)
 Small template size
 Simple user – system interaction
Inspecting the list above, voice-scan mainly suffers from lower accuracy and higher
template size. Facial-scan may not provide the required accuracy due to changes in
environmental conditions and user appearance. Although iris is the most reliable
biometric, high cost of acquisition devices used in order to scan iris is the biggest
handicap of this technology. Finger-scan has a very high accuracy with simple user
system interaction and small template size. Nevertheless, physical work and age may
cause people not to have clear fingerprints. Additionally, possible dirt and grease on
fingerprints may reduce the system accuracy. Were the area of fingerprint larger,
finger-scan technology might suffer less from effects of dirt, physical work and age
on fingerprints. Palm, on the other hand, provides a large area for feature extraction
and seems to suffer less from factors that reduce the accuracy in finger-scan
technology. Moreover, large area of palm enables utilization of low resolution images
resulting in cheaper acquisition devices. Furthermore, a very small FTER is expected
in palmprint-scan applications because it is easy to correctly place palm on a desired
platform. Due to the same reason, it is possible to have a system with simple user –
system interaction. Additionally, palmprint-scan is a promising biometric technology
to have high accuracy because palmprint is covered with a similar skin as fingerprint.
Finally, palmprint-scan technology has high user acceptance which is quite necessary
for the technology to spread out. As it is seen, palmprint possesses the most of desired
features therefore; it may be used as a biometric. The next chapter will describe some
palmprint recognition algorithms in the literature and will explain results obtained in
these algorithms.

PIET Page 18
CHAPTER 3
EXISTING PALMPRINT RECOGNITION
ALGORITHMS

PIET Page 19
Researchers noticing the increase in biometric revenues in last years and realizing the
advantages of palmprint scan-technology mentioned in the previous chapter started to
develop algorithms to be used in palmprint recognition. Researchers’ interest in
palmprint recognition algorithms has significantly increased especially in last three
years. Due to the fact that the palmprint recognition is a relatively new field of
biometrics, there is a problem related to the utilization of a common palmprint
database in order to be able to compare the performance of different algorithms.
Nevertheless, The Hong Kong Polytechnic University Palmprint Database is the most
commonly used palmprint database. It is here worth giving brief information about
this database before explaining some of the studies on palmprint recognition. The
Hong Kong Polytechnic University Palmprint Database contains 600 grayscale
images corresponding to 100 different palms in Bitmap image format. Palm images
have a resolution of 284x384 pixels with 256 gray levels. Six samples from each of
these palms were collected in two sessions, where 3 samples were captured in the first
session and the other 3 in the second session. The average interval between the first
and the second collection was two months. The palmprint images in the database are
labeled as "PolyU_xx_N.bmp", where the "xx" is the unique palm identifier (ranges
from 00 to 99), and "N" is the index of each palm (ranges from 1 to 6), the palmprints
indexed from 1 to 3 are collected in the first session and 4 to 6 in the second session.
[5] Figure 3.1 shows a schematic diagram of the online palmprint capture device used
to acquire these palm images. The palmprint capture device includes ring source,
CCD camera, lens, frame grabber, and A/D (analogue-todigital) converter. To obtain
a stable palmprint image, a case and a cover are used to form a semi-closed
environment, and the ring source provides uniform lighting conditions during
palmprint image capturing. Also, six pegs on the platform, which is demonstrated in
Figure 3.2, serve as control points for the placement of the user’s hands. The A/D
converter directly transmits the images captured by the CCD camera to a computer.
[6]

PIET Page 20
Figure 3.1:Schematic Diagram of Palmprint Acquisition System [7]
Figure 3.2:Pegs and the Cropped Area of the Palm [7]

PIET Page 21
Various algorithms have been developed to be used in palmprint recognition.
Developed algorithms mainly include different methods for feature extraction and
distance matching. From now on, some of the methods developed for palmprint
recognition will be mentioned and their results will be discussed.
Fang Li et al. [8] proposed an approach utilizing Line Edge Map (LEM) of palmprint
as the feature and Hausdorff distance as the distance matching algorithm. In this
study, Line segment Hausdorff distance (LHD) and Curve segment Hausdorff
distance (CHD) are explored to match two sets of lines and two sets of curves. They
carried out an identification experiment on The Hong Kong Polytechnic University
Palmprint Database. 200 palm images, i.e. 2 palm images for each person, have been
randomly selected in order to test the system performance. They reserved one palm
image for each individual as a template, and used remaining palm images as test
images to be identified. Fang Li et al. [9] later proposed the utilization of Modified
Line segment Hausdorff distance (MLHD) as the distance matching algorithm. In this
study, 2-D lowpass filter is applied to sub-image extracted from the captured hand
image. The result is subtracted from the image in order to decrease the non-uniform
illumination effect resulting from the projection of a 3-D object onto a 2-D image.
After line detection, contour and line segment generation steps, each line on a palm is
represented using several straight line elements. Finally, MLHD is used in order to
measure the similarity between two palm images. Performance of this and some other
palmprint identification methods are tabulated in Table 3-1.
Algorithms employing neural networks have also been developed. Li Shang et al. [13]
suggested the usage of radial basis probabilistic neural network (RBPNN). The
RPBNN is trained by the orthogonal least square algorithm (OLS) and its structure is
optimized by the recursive OLS algorithm (ROLSA). A fast fixed-point algorithm is
used for independent component analysis. The Hong Kong Polytechnic University
Palmprint Database is used to test the developed palmprint recognition algorithm.
After tests performed on this database, recognition rates between % 95 and % 98 are
obtained.

PIET Page 22
CHAPTER 4
PROPOSED SYSTEM

PIET Page 23
Figure 4.1:Block diagram of palm print verification system
A palm print verification system is a one-to-one matching process. It matches a
person’s claimed identity to enrolled pattern. There are two phases in the system:
enrollment and verification. Both phases comprise two sub-modules: preprocessing
for palm print localization, enhancement and feature extraction for moment features
extraction. However, verification phase consists of an additional sub module,
classification, for calculating dissimilarity matching of the palm print. Figure 4 shows
the palm print verification system block diagram.
At the enrollment stage, a set of the template images represented by moment features
is labeled and stored into a database. At the verification stage, an input image is
converted into a set of moment features, and then is matched with the claimant’s palm
print image, based on the ID, stored in the database to gain the dissimilarity measure
by computing Euclidean distance metric. We used this distance metric instead of more
Palm ROI
Template stored in
database
Features
Feature
extraction
Preprocessin
g
Dissimilarity
matching
Features
Palm
ROI
Preprocessin
g
Feature
extraction
Threshold
ENROLMENT
IDENTIFICATION

PIET Page 24
complex classification algorithm (e.g. neural network) because we were just focusing
on the feature extracting rather than the classification. Finally, the dissimilarity
measure is compared to a pre-defined threshold to determine whether a claimant
should be accepted. If the dissimilarity measure below the predefined threshold value,
the palm print input is verified possessing same identity as the claimed identity
template and the claimant is accepted.
Also, six pegs on the platform, which is demonstrated in Figure 6 , serve as control
points for the placement of the user’s hands. The A/D converter directly transmits the
images captured by the CCD camera to a computer.
Figure 4.2:Database examples
4.1 ROI EXTRACTION
To extract the region of interest (ROI) from the palm images, the following steps are
to be followed:
 Binarization
 Contouring
 Selecting reference point
 Cropping ROI
4.1.1 BINARIZATION
For binarization of the image, we use the global thresholding. Here we find the global
threshold value of the image and compare every pixel of image with the threshold

PIET Page 25
value. If the value is less than the threshold, the pixel value is set to zero; else it is set
to one. For the input image I of the size N×N, global threshold G_Threshold can be
determined using
∑ ∑
……………………………………………...……….(6)
where I(i,j) is intensity value of pixel at position (i,j) of hand image. This threshold is
used to obtain the binarized image BI using
{ ………………………………..…(7)
The following figure shows the image of palm and its corresponding binarized image.
This is then further processed using morphological methods for better results.
Figure 4.3:Binarized image
4.1.2 CONTOURING
After getting the binarized image from the palm print image it is then converted to
contour image by using the contour function. The following image shows the
binarized image and its corresponding contour image.

PIET Page 26
Figure 4.4:Palm print contour
4.1.3 SELECTING REFERANCE POINT
Now to select the square or rectangle region on the palm we require a reference point
on the contour. For this we take the distance transform of the contour image. The
distance transform give the distance of the pixel from the nearest non zero value pixel.
From that plot we take the pixel with the highest value, the center most pixel. This
will be the refine pixel to crop ROI. The following figure shows the distance
transform of the palm contour.
Figure 4.5:Distance transform of contour image

PIET Page 27
4.1.4 CROPING ROI
Now the reference point obtained from the distance transform is taken on the palm
print image and from the reference of that point a square or rectangle image is
cropped. The following figure shows the cropped ROI.
Figure 4.6:Region to be cropped
The extracted ROI is then preprocessed and enhanced to make it appropriate for
feature extraction. These are then stored and used for feature matching function for
identification and verification purpose. Figure below shows the square ROI example
that will be used for it.
Figure 4.7:ROI
4.2 ENHANCEMENT OF ROI
The extracted palm print is having non-uniform brightness because of non-uniform
reflection from the relatively curvature of the palm. In order to obtain well distributed
texture image following operations are applied on extracted palm print.

PIET Page 28
(a) (b)
(c) (d)
Figure 4.8:(a) palm print ROI (b) coarse reflection, (c) uniform brightness palm print image, (d)
Enhanced palm print image
The palm print is divided into sub blocks and mean of each sub block is calculated.
Now this image of sub blocks with mean values is subtracted from the original image.
This results in a uniform brightness image. But this is too dark. Now the local
histogram of this image is done to enhance the image.
 Palm print is shrinked to the 1/32th
size and zoomed out to 32 times.
This is done with bicubic parameter so as to give estimated coarse
reflection of the image.
 This coarse reflection of palm print is then subtracted from the original
ROI to get an uniform brightness image, as shown in figure (c)
The local histogram equalization of this uniform brightness image is done to get
enhanced ROI for further processing and feature extraction.

PIET Page 29
4.3 FEATURE EXTRACTION AND CODING
In this block, relevant features are extracted from the central palm area obtained in the
previous block. Then these extracted features are coded and the mathematical
representation of the palm is obtained. Developing a palmprint recognition algorithm
that can successfully discriminate between palm images of low resolution is a big
advantage from the practical side of view. This is because; since the developed
algorithm does not require high resolution images, there is no need in high resolution
capturing devices which are quite expensive. Being aware of the fact that the cost of
the capturing device plays an important role in determining the total cost of the
developed biometric system, it can be said that the total cost of the system can be
significantly decreased by decreasing the cost of the capturing device. It should be
obvious that low-cost products are easy to market therefore; developing an algorithm
capable of working accurately with low resolution images is very important. Principal
lines, wrinkles, ridges, minutiae points and texture are considered to be relevant
features for a palm (Three principal lines, named as Life Line, Heart Line and Head
Line, and some wrinkles in a palm are shown in Figure 4.14). However, these relevant
features require different resolutions in order to be extracted. In general, principal
lines and wrinkles can be extracted from low resolution images, whereas ridges and
minutiae points need higher resolution. Table 4-1 shows approximate required
resolutions to extract principle lines, wrinkles and ridges texture in dots per inch (dpi).
As it is seen from the table, principal lines can be obtained even in quite low
resolution images. Considering the cost of the biometric system, principal lines may
be thought to be very suitable to be used in the developed algorithm. Although
principal lines can be extracted with algorithms such as the stack filter, they do not
have the uniqueness property, that is, different individuals may have similar principle
lines. This problem has been demonstrated in Figure 4.15. Palm images in (a), (b) and
(c); (d), (e) and (f); and (g), (h) and (i) are very similar to each other; however they
belong to different individuals. Wrinkles may also be thought to be employed,
nevertheless; usage of wrinkles is questionable due to the permanence property,
because wrinkles are subject to change with time. Furthermore, extracting wrinkles
accurately is not an easy task. Due to reasons mentioned above, texture analysis has
been selected to be used in the developed algorithm. [6]

PIET Page 30
Figure 4.9:Principle Lines and Wrinkles in a Palm [20]
Table 4.1: DPI REQUIREMENTS
PALM PRINT FEATURES REQUIRED RESOLUTION (in dpi)
Principal Lines ≥75
Wrinkles ≥100
Ridges Texture ≥125

PIET Page 31
Figure 4.10:Three Sets of Palmprint Images with Similar Principal Lines from Different People
4.4 ZERNIKE MOMENTS
The kernel of Zernike moments is a set of orthogonal Zernike polynomials defined
over the polar coordinate space inside a unit circle. The two dimensional Zernike
moments of order p with repetition q of an image intensity function f(r,θ) are defined
as:
∫ ∫ | | ……………………………….(4.1)

PIET Page 32
Where Zernike polynomials vpq(r,θ) are defined as:
√ …………………………….………………..(4.2)
And the real-valued radial polynomials, Rpq(r), is defined as follows:
∑
| |
(
| |
) (
| |
)
………………………………..(4.3)
where 0 ≤ |q| ≤ p and p - |q| is even.
If N is the number of pixels along each axis of the image, then the discrete
approximation of equation (1) is given as:
∑ ∑ ( ) ; 0≤ rij ≤1 .....................................(4.4)
where λ(p,N) is normalizing constant and image coordinate transformation to the
interior of the unit circle is given by
√ ; ( );
xi = c1 i + c2 ;
yj = c1 j + c2……………………………………………………………………………………………………..……………… (4.5)
Since it is easier to work with real functions, Zpq is often split into its real and
imaginary parts, Zc
pq, Zs
pq as given below:
∫ ∫ ………………………..….(4.6)
∫ ∫ ………………………...….(4.7)
where p ≥ 0 , q > 0 .
For the implementation, square image (N x N) is transformed and normalized over a
unit circle; i.e. x2
+ y2
≤1 , which the transformed unit circle image is bounding the

PIET Page 33
square image. Figure 3 shows the square-to-circular transformation. In this
transformation,
√
√
………………………………………... (4.8)
Therefore,
√
√
and
√
√
……………………………………….(4.9)
Figure 4.11:Square to circular transform.
These features are then to be matched with the test image. For that purpose we use
the Euclidean distance. The Euclidean distance between points p and q is the length of
the line line segment ̅̅̅.
In Cartesian coordinates, if p = (p1 ,p2,...,pn) and q = (q1 ,q2,...,qn) are two points in
Euclidean n-space, then the distance from p to q is given by
‖ ‖
√

PIET Page 34
The features of the test image and the database are compared using the Euclidean
distance. The image with the least Euclidean distance is considered as the matched
result.

PIET Page 35
CHAPTER 5
RESULTS

PIET Page 36
Experiments were conducted by using a set of database consisting of 20 different
classes of palm prints. Each hand has 10 palm print images. 7 from each are used for
training the system, total 140 images. And other 3 images were used for testing
purpose, total 60 images.
One test image is compared with all the train images to find the corresponding
matching image is. Figure 5.1 to 5.6 shows the minimum distances between the palm
prints of the test image and all train images. The minimum distances are obtained in
the region where the corresponding train images are located. Among them one is
selected as the matched image.
Figure 5.1:Minimum distance for test image 8

PIET Page 37

PIET Page 38

PIET Page 39
Figure 5.7:Value of minimum distance for test image 20
Figure 5.8:Train index values for corresponding test images

PIET Page 40
Figure 5.9:Minimum distance graph for all test images
Figure 5.10:False matched images

PIET Page 41
Figure 5.12:Result of thresholding
Figure 5.12 shows the result of Thresholding. The red colored bars have distances
higher then threshold, thus are eliminated, the green colored bars are truly detected
images. The blue colored bars are false matches, and are less than threshold, thus
giving false matches.
Figure 5.12 displays the histogram of the smallest distance, the distance between the
test images and the most similar templates, for correct matches. Figure 5.13 shows the
histogram of the second smallest distance, the distance between the test images and
the second most similar templates. It is here worth noting that the difference between
the smallest distance and the second smallest distance gives an idea about the
reliability of the identification; that is the bigger the difference is, the more reliable
the identification is. Let the reliability of identification ratio, RI, be defined as the
ratio of this difference to the smallest distance, as in Equation (5.1). The histogram of
the reliability of identification ratio is depicted in Figure 5.14.
RI = ……..…………………………(5.1)

PIET Page 42
Figure 5.13:Smallest distance histogram
Figure 5.14:Second smallest distance histogram

PIET Page 43
Figure 3.15:Reliability of identification
Here database was used and experiment was conducted using different settings of
feature vectors based on the order of ZM and the efficiency is calculated by Euclidean
distance. The efficiency is calculated as the no. of correctly matched images from the
total no. of images. This is then compared to the legendre moments for the same
moment orders. The comparison is shown in Table 5.1
Table 5.1: EFFICIENCY
MOMENT ORDERS ZERNIKE (%) LEGENDRE (%)
0,1 68.3333 73.3333
0,1,2,3 71.6666 66.6666
0,1,2,3,4,5 78.3333 55.0000
0,1,2,3,4,5,6,7 85.0000 66.6666
0,1,2,3,4,5,6,7,8,9 71.6666 33.3333
0,1,2,3,4,5,6,7,8,9,10,11 65.0000 40.0000
The 7th order gives the maximum efficiency of 85%. The other results are then shown
are of this moment order. On the other hand the Legendre moments gives random
change in the efficiency. After 7th
order the efficiency starts reducing due to the noise
affecting the moment calculations.

PIET Page 44
CHAPTER 6
CONCLUSION AND FUTURE SCOPE

PIET Page 45
6.1 CONCLUSION
A palm print identification system using Zernike features is proposed. The proposed
palm print based verification system has the following characteristics:
 Constraint free image acquisition: The device used for acquiring hand image
from user should be constraint free. So that physically challenged or injured
people can provide biometric sample.
 Robust to translation and rotation: The system should be able to extract palm
print independent to translation and/or rotation of hand on scanner surface.
 Low cost scanner: The device used should be economic and easily deployable.
The performance of Zernike moments palm print authentication system was presented
in this thesis. The Zernike moments of order 7 has the best performance among all the
moments. Its efficiency is 85%, which represents the overall performance of this palm
print authentication system. The proposed algorithms, orthogonal moments, possess
some advantages: orthogonality and geometrical invariance. Thus, they are able to
minimize information redundancy as well as increase the discrimination power.
6.2 FUTURE SCOPE
Although performance of the proposed system is satisfactory, it can further be
improved with small modifications and addition preprocessing of hand images.
Also use of circular ROI can be possible by modification in the radial polynomial of
Zernike moments which can make it better rotational invariant.

PIET Page 46
REFERENCES

PIET Page 47
References
PAPERS:
[1] John D. Woodward, Jr., Nicholas M. Orlans, Peter T. Higgins, “Biometrics”,
McGraw-Hill, 2003.
[2] Ruud M. Bolle, Jonathan H. Connell, Sharath Pankanti, Nalini K. Ratha, Andrew
W. Senior, “Guide to Biometrics”, Springer, 2004.
[3] Samir Nanavati, Michael Thieme, Raj Nanavati, “Biometrics”, Wiley, 2002.
[4] “Biometrics Glossary”, retrieved 10 January 2007, from
http://www.biometricscatalog.org/biometrics/GlossaryDec2005.pdf
[5] “PolyU Palmprint Palmprint Database”
http://www.comp.polyu.edu.hk/~biometrics/
[6] David Zhang, Wai-Kin Kong, Jane You, Michael Wong, “Online Palmprint
Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 25, No. 9, pp 1041-1050, September 2003.
[7] M. Wong, D. Zhang, W.-K. Kong and G. Lu, “Real-time Palmprint Acquisition
System Design”, IEE Proc.-Vis. Image Signal Process., Vol. 152, No. 5, October
2005.
[8] Fang Li, Maylor K.H. Leung, Xiaozhou You, “Palmprint Identification Using
Hausdorff Distance”, 2004 IEEE International Workshop on Biomedical Circuits &
Systems, 2004.
74
[9] Fang Li, Maylor K.H. Leung, Xiaozhou You, “Palmprint Matching Using Line
Features”, ICACT 2006, 20-22 February 2006.
[10] N. Duta, A.K. Jain, “Matching of Palmprints”, Pattern Recognition Letters I,
Vol. 23, pp 477-485, 2002.
[11] J. You, W. X. Li and D. Zhang, “Hierarchical Palmprint Identification via
Mutiple Feature Extraction”, Pattern Recognition Vol. 35, No. 4, pp 847-859, 2002.
[12] D. Zhang, W. Shu, “Two Novel Characteristics in Palmprint Verification:
Datum Point Invariance and Line Feature Matching”, Pattern Recognition, vol 33,
no. 4, pp 691-702, 1999.
[13] Li Shang, De-Shuang Huang, Ji-Xiang Du, Chun-Hou Zheng, “Palmprint
Recognition using FastICA algorithm and radial basis probabilistic neural network”,
2005.

PIET Page 48
[14] Xiang-Qian Wu, Kuan-Quan Wang, David Zhang, “Palmprint Recognition
Using Valley Features”, Proceedings of the Fourth International Conference on
Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005._
[15] A.K. Jain, S. Prabhakar, L. Hong, S. Pankanti, “Filterbank-Based Fingerprint
Matching”, IEEE Trans. Image Process., Vol. 9, No. 5, pp 846–859, 2000.
[16] L. Hong, Y. Wan, A. Jain, “Fingerprint Image Enhancement Algorithm and
Performance Evaluation”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 20, No. 8, pp
777–789, 1998.
[17] C.J. Lee, S.D. Wang, “Fingerprint Feature Extraction Using Gabor Filters”,
Electron. Lett., Vol. 35, No. 4, pp 288–290, 1999.
75
[18] Xiangqian Wu, Kuanquan Wang, Fengmiao Zhang, David Zhang, “Fusion of
Phase and Orientation Information for Palmprint Authentication”, 2005.
[19] Ajay Kumar, Helen C. Shen, “Palmprint Identification using PalmCodes”,
Proceedings of the Third International Conference on Image and Graphics, 2004.
[20] Wai King Kong, David Zhang, Wenxin Li, “Palmprint Feature Extraction Using
2-D Gabor Filters”, Pattern Recognition, Vol. 36, pp 2339- 2347, 2003.
[21] M.J. Lyons, J. Budynek, S. Akamatsu, “Automatic Classification of Single
Facial Images”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 21, No. 12, pp 1357–
1362, 1999.
[22] B. Duc, S. Fischer, J. Bigun, “Face Authentication with Gabor Information on
Deformable Graphs”, IEEE Trans. Image Process., Vol. 8, No. 4, pp 504–516, 1999.
[23] Y. Adini, Y. Moses, S. Ullman, “Face Recognition: The Problem of
Compensation for Changes in Illumination Direction”, IEEE Trans. Pattern Anal.
Mach. Intell., Vol. 19, No. 7, pp 721–732, 1997.
[1] Ying-Han Pang, Andrew T.B.J, David N.C.L, Hiew Fu San “Palm print
Verification with Moments” Journal of WSCG, Vol.12, No.1-3, ISSN 1213-
6972 WSCG’2004, February 2-6
[2] Amir Tahmasbi, FatemehSaki,ShahriarB.Shokouhi “Classification of benign
and malignant masses based on Zernike moments” Elsevier- Computers in
Biology and Medicine 2011, june 14
[3] Madasu Hanmandlu, Neha Mittal, Ankit Gureja, Ritu Vijay “A
Comprehensive Study of Palmprint based Authentication” International
Journal of Computer Applications (0975 – 8887) vol. 37 – No.2, January 2012
[4] Atif Bin Mansor, Hassan Masood, Mustaffa Mumtaz, Shoab A. Khan “ A
feature level multimodal approach for palmprint identification using

PIET Page 49
directional subband energies” Elsevier – Journal of network and Computer
Applications.
BOOKS:
[1] Flusser, Jan, Suk, Tomáš and Zitová, Barbara. “Moments and Moment
Invariants.”
[2] Liao, Simon Xin meng. “Image Analysis by moments.”
WEBSITES:
[1] http://en.wikipedia.org/wiki/Moment_(mathematics)
[2] http://www4.comp.polyu.edu.hk/~biometrics/index_db.htm
http://www.si2.org/openeda.si2.org/dfmcdictionary/index.php/Zernike_Pol
ynomials

PIET Page 50
APPENDIX

PIET Page 51
APPENDIX-A

SHARMA THESIS1

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (16)

Similar a SHARMA THESIS1

Similar a SHARMA THESIS1 (20)

SHARMA THESIS1