International Journal of Biometrics and Bioinformatics (IJBB)

---

INTERNATIONAL JOURNAL OF BIOMETRICS
AND BIOINFORMATICS (IJBB)







VOLUME 5, ISSUE 1, 2011

EDITED BY
DR. NABEEL TAHIR








ISSN (Online): 1985-2347
International Journal of Biometrics and Bioinformatics (IJBB) is published both in traditional paper
form and in Internet. This journal is published at the website http://www.cscjournals.org,
maintained by Computer Science Journals (CSC Journals), Malaysia.


IJBB Journal is a part of CSC Publishers
Computer Science Journals
http://www.cscjournals.org

---

INTERNATIONAL JOURNAL OF BIOMETRICS AND
BIOINFORMATICS (IJBB)

Book: Volume 5, Issue 1, March 2011
Publishing Date: 04-04-2011
ISSN (Online): 1985-2347

This work is subjected to copyright. All rights are reserved whether the whole or
part of the material is concerned, specifically the rights of translation, reprinting,
re-use of illusions, recitation, broadcasting, reproduction on microfilms or in any
other way, and storage in data banks. Duplication of this publication of parts
thereof is permitted only under the provision of the copyright law 1965, in its
current version, and permission of use must always be obtained from CSC
Publishers.



IJBB Journal is a part of CSC Publishers
http://www.cscjournals.org

© IJBB Journal
Published in Malaysia

Typesetting: Camera-ready by author, data conversation by CSC Publishing Services – CSC Journals,
Malaysia



CSC Publishers, 2011

---

EDITORIAL PREFACE

This is the first issue of volume five of International Journal of Biometric and Bioinformatics
(IJBB). The Journal is published bi-monthly, with papers being peer reviewed to high international
standards. The International Journal of Biometric and Bioinformatics is not limited to a specific
aspect of Biology but it is devoted to the publication of high quality papers on all division of Bio in
general. IJBB intends to disseminate knowledge in the various disciplines of the Biometric field
from theoretical, practical and analytical research to physical implications and theoretical or
quantitative discussion intended for academic and industrial progress. In order to position IJBB as
one of the good journal on Bio-sciences, a group of highly valuable scholars are serving on the
editorial board. The International Editorial Board ensures that significant developments in
Biometrics from around the world are reflected in the Journal. Some important topics covers by
journal are Bio-grid, biomedical image processing (fusion), Computational structural biology,
Molecular sequence analysis, Genetic algorithms etc.

The initial efforts helped to shape the editorial policy and to sharpen the focus of the journal.
Starting with volume 5, 2011, IJBB appears in more focused issues. Besides normal publications,
IJBB intend to organized special issues on more focused topics. Each special issue will have a
designated editor (editors) – either member of the editorial board or another recognized specialist
in the respective field.

The coverage of the journal includes all new theoretical and experimental findings in the fields of
Biometrics which enhance the knowledge of scientist, industrials, researchers and all those
persons who are coupled with Bioscience field. IJBB objective is to publish articles that are not
only technically proficient but also contains information and ideas of fresh interest for International
readership. IJBB aims to handle submissions courteously and promptly. IJBB objectives are to
promote and extend the use of all methods in the principal disciplines of Bioscience.

IJBB editors understand that how much it is important for authors and researchers to have their
work published with a minimum delay after submission of their papers. They also strongly believe
that the direct communication between the editors and authors are important for the welfare,
quality and wellbeing of the Journal and its readers. Therefore, all activities from paper
submission to paper publication are controlled through electronic systems that include electronic
submission, editorial panel and review system that ensures rapid decision with least delays in the
publication processes.

To build its international reputation, we are disseminating the publication information through
Google Books, Google Scholar, Directory of Open Access Journals (DOAJ), Open J Gate,
ScientificCommons, Docstoc and many more. Our International Editors are working on
establishing ISI listing and a good impact factor for IJBB. We would like to remind you that the
success of our journal depends directly on the number of quality articles submitted for review.
Accordingly, we would like to request your participation by submitting quality manuscripts for
review and encouraging your colleagues to submit quality manuscripts for review. One of the
great benefits we can provide to our prospective authors is the mentoring nature of our review
process. IJBB provides authors with high quality, helpful reviews that are shaped to assist authors
in improving their manuscripts.


Editorial Board Members
International Journal of Biometric and Bioinformatics (IJBB)

---

EDITORIAL BOARD

EDITOR-in-CHIEF (EiC)

Professor João Manuel R. S. Tavares
University of Porto (Portugal)


ASSOCIATE EDITORS (AEiCs)


Assistant Professor. Yongjie Jessica Zhang
Mellon University
United States of America

Professor. Jimmy Thomas Efird
University of North Carolina
United States of America

Professor. H. Fai Poon
Sigma-Aldrich Inc
United States of America

Professor. Fadiel Ahmed
Tennessee State University
United States of America

Mr. Somnath Tagore (AEiC - Marketing)
Dr. D.Y. Patil University
India

Professor. Yu Xue
Huazhong University of Science and Technology
China

Associate Professor Chang-Tsun Li
University of Warwick
United Kingdom

Professor. Calvin Yu-Chian Chen
China Medical university
Taiwan



EDITORIAL BOARD MEMBERS (EBMs)


Dr. Wichian Sittiprapaporn
Mahasarakham University
Thailand

Assistant Professor. M. Emre Celebi
Louisiana State University
United States of America

---

Dr. Ganesan Pugalenthi
Genome Institute of Singapore
Singapore


Dr. Vijayaraj Nagarajan
National Institutes of Health
United States of America

Dr. Paola Lecca
University of Trento
Italy

Associate Professor. Renato Natal Jorge
University of Porto
Portugal

Assistant Professor. Daniela Iacoviello
Sapienza University of Rome
Italy

Professor. Christos E. Constantinou
Stanford University School of Medicine
United States of America

Professor. Fiorella SGALLARI
University of Bologna
Italy

Professor. George Perry
University of Texas at San Antonio
United States of America

Assistant Professor. Giuseppe Placidi
Università dell'Aquila
Italy

Assistant Professor. Sae Hwang
University of Illinois
United States of America

Associate Professor Quan Wen
University of Electronic Science and Technology
China

Dr. Paula Moreira
University of Coimbra
Portugal

Dr. Riadh Hammami
Laval University
Canada

---

Mansi Jhamb & Vinod Kumar Khera

IRIS Based Human Recognition System


Mansi Jhamb
mansi.jhamb@gmail.com
USIT, Guru Gobind Singh Indraprastha

University, Delhi, India


Vinod Kumar Khera

Guru Tegh Bahadur Institute of
vinodkhera@gmail.com
Technology Guru Gobind Singh

Indraprastha University, Delhi, India

Abstract

The paper explores iris recognition for personal identification and verification. In this paper a new
iris recognition technique is proposed using (Scale Invariant Feature Transform) SIFT. Image-
processing algorithms have been validated on noised real iris image database. The proposed
innovative technique is computationally effective as well as reliable in terms of recognition rates.

Keywords: Iris Recognition, Hough Transform, SIFT, Key-Points


1 INTRODUCTION
Today, biometric recognition is a common and reliable way to authenticate the identity of a living
person based on physiological or behavioral characteristics. A physiological characteristic is
relatively stable physical characteristics, such as fingerprint, iris pattern, facial feature, hand
silhouette, etc. This kind of measurement is basically unchanging and unalterable without
significant duress. A behavioral characteristic is more a reflection of an individual’s psychological
makeup as signature, speech pattern, or how one types at a keyboard. The degree of intra-
personal variation in a physical characteristic is smaller than a behavioral characteristic. For
examples, a signature is influenced by both controllable actions and less psychological factors,
and speech pattern is influenced by current emotional state, whereas fingerprint template is
independent. Nevertheless all physiology-based biometrics don’t offer satisfactory recognition
rates (false acceptance and/or false reject rates, respectively referenced as FAR and FRR). The
automated personal identity authentication systems based on iris recognition are reputed to be
the most reliable among all biometric methods: we consider that the probability of finding two
people with identical iris pattern is almost zero [1]. That’s why iris recognition technology is
becoming an important biometric solution for people identification in access control as networked
access to computer application [2]. Compared to fingerprint, iris is protected from the external
environment behind the cornea and the eyelid. No subject to deleterious effects of aging, the
small-scale radial features of the iris remain stable and fixed from about one year of age
throughout life. This paper is divided into 4 main parts. The Section 1 introduces what is the
position of iris technology in personal authentication. In the Section 2, we sum up the state of the
art in the domain of iris recognition. The more widely known iris recognition system developed by
J.Daugman [4] is taken as reference for comparison. The Section 3 presents in details our
approach, and discusses the different issues we chose. At last a conclusion is done in Section 4,
which tasks about the next considerations for the improvement of the proposed solution.

2. LITERATURE SURVEY
The French ophthalmologist Alphonse Bertillon seems to be the first to propose the use of iris
pattern (color) as a basis for personal identification [3]. In 1981, after reading many scientific
reports describing the iris great variation, Flom and San Francisco ophthalmologist Aran Safir
suggested also using the iris as the basis for a biometric. In 1987, they began collaborating with
computer scientist John Daugman of Cambridge University in England to develop iris
identification software who published his first promising results in 1992 [4]. Later on a little similar

International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (1) : 2011
1

---

Mansi Jhamb & Vinod Kumar Khera

works have been investigated, such as R.Wildes’ [5], W.Boles’ [6] and R.Sanchez- Reillo’s [7]
systems, which differ both in the iris features representation (iris signature) and pattern matching
algorithms. R.Wildes’ solution includes (i) a Hough transform for iris localization, (ii) Laplacian
pyramid(multi-scale decomposition) to represent distinctive spatial characteristics of the human
iris, and (iii) modified normalized correlation for matching process. W.Boles’ prototype operates in
building (j) a one dimensional representation of the gray level profiles of the iris followed by
obtaining the wavelet transform zero-crossings of the resulting representation, and (jj) original
dissimilarity functions that enable pertinent information selection for efficient matching
computation. To finish J.Daugman’s and R.Sanchez-Reillo’s systems are implemented exploiting
(l) integrodifferential operators to detect iris inner and outer boundaries, (ll) Gabor filters to extract
unique binary vectors constituting iriscodeTM, and (lll) a statistical matcher (logical exclusive OR
operator) that analyses basically the average Hamming distance between two codes (bit to bit
test agreement). Because of unified reference database of iris images does not exist, a classic
performance comparison of the described systems is not trivial. However in terms of recognition
rates (FAR, FRR), the commercial success of the patented Daugman’s system speak in his favor.
Indeed Daugman’s mathematical algorithms have been contributing to a commercial solution
patented by IriScan Inc. This biometric identification platform processes iris recognition through (i)
a specific optical unit that enables noninvasive acquisition of iris images, and (ii) a data
processing unit. Although capturing a well-defined image of the iris while not interacting actively
with the device seems to be one the major challenge we encountered for iris recognition system
design, our research focus on the second block both in charge of (j) the enrolment process,
and (jj) the matching which quantifies the similitude between two biometric templates.

3. PROPOSED APPROACH
Previous work on iris recognition, derived from the information found in the open literature, led us
to suggest a few possible improvements. For justification of these new concepts we implemented
in Matlab/C .The algorithm used is as follows:

•
Image Acquisition
•
Iris Localization.
•
Find the darkest point of image (referred as black hole) in the global image analysis.
•
Determine a range of darkness (based on 1) designated as the threshold value (t) for
identification of black holes.
•
Determine the number of black holes and their coordinates according to the predefined
threshold. Calculate the centre of mass of these black holes.
•
Construct a L x L region centred at the estimated centroid.
•
Repeat step 3 to improve the estimation of actual centroid of pupil.

•
Find key points using SIFT.
•
Match the key points of the input image with the key points of images in database.
The algorithm is beautifully explained by following algorithmic flow chart ,figure 1

FIGURE 1: Iris Recognition: The Process

3.1 IMAGE ACQUISITION
One of the major challenges of automated iris recognition is to capture a high-quality image of the
iris while remaining noninvasive to the human operator. Given that the iris is a relatively small
(typically about 1 cm in diameter), dark object and that human operators are very sensitive about

International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (1) : 2011
2

---

Mansi Jhamb & Vinod Kumar Khera

their eyes, this matter requires careful engineering. Several points are of particular concern. First,
it is desirable to acquire images of the iris with sufficient resolution and sharpness to support
recognition. Second, it is important to have good contrast in the interior iris pattern without
resorting to a level of illumination that annoys the operator, i.e., adequate intensity of source
(W/cm ) constrained by operator comfort with brightness (W/sr-cm ). Third, these images must be
well framed (i.e., centered) without unduly constraining the operator (i.e., preferably without
requiring the operator to employ an eye piece, chin rest, or other contact positioning that would
be invasive). Further, as an integral part of this process, artifacts in the acquired images (e.g.,
due to specular reflections, optical aberrations, etc.) should be eliminated as much as possible.
Schematic diagrams of two image-acquisition rigs that have been developed in response to these
challenges. The acquired Image is as shown in figure 2 below:



FIGURE 2: Acquired Image

3.2 IRIS LOCALIZATION
Without placing undue constraints on the human operator, image acquisition of the iris cannot be
expected to yield an image containing only the iris. Rather, image acquisition will capture the iris
as part of a larger image that also contains data derived from the immediately surrounding eye
region. Therefore, prior to performing iris pattern matching, it is important to localize that portion
of the acquired image that corresponds to an iris. In particular, it is necessary to localize that
portion of the image derived from inside the limbus (the border between the sclera and the iris)
and outside the pupil. Further, if the eyelids are occluding part of the iris, then only that portion of
the image below the upper eyelid and above the lower eyelid should be included. Typically, the
limbic boundary is imaged with high contrast, owing to the sharp change in eye pigmentation that
it marks. The upper and lower portions of this boundary, however, can be occluded by the
eyelids. The papillary boundary can be far less well defined. The image contrast between a
heavily pigmented iris and its pupil can be quite small. Further, while the pupil typically is darker
than the iris, the reverse relationship can hold in cases of cataract: the clouded lens leads to a
significant amount of backscattered light. Like the pupillary boundary, eyelid contrast can be quite
variable depending on the relative pigmentation in the skin and the iris. The eyelid boundary also
can be irregular due to the presence of eyelashes. Taken in tandem, these observations suggest
that iris localization must be sensitive to a wide range of edge contrasts, robust to irregular
borders, and capable of dealing with variable occlusion. The systems differ mostly in the way that
they search their parameter spaces to fit the contour models to the image information. To
understand how these searches proceed, let I(x,y) represent the image intensity value at location
(x,y) and let circular contours (for the limbic and papillary boundaries) be parameterized by center
location (xc,yc) and radius r. The Daugman system fits the circular contours via gradient ascent
on the parameters (xc,yc,r) so as to maximize

2
/ 2σ
−
−
Where
(r ro)2
G(r) = 1
( /
2σ ∏ )σ
is a radial Gaussian with center ro and standard
deviation σ that smooths the image to select the spatial scale of edges under consideration * ,
symbolizes convolution, ds is an element of circular arc, and division by 2πr serves to normalize
the integral. In order to incorporate directional tuning of the image derivative, the arc of integration
ds is restricted to the left and right quadrants (i.e., near vertical edges) when fitting the limbic
boundary. This arc is considered over a fuller range when fitting the pupillary boundary; however,

International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (1) : 2011
3

---

Mansi Jhamb & Vinod Kumar Khera

the lower quadrant of the image is still omitted due to the artifact of the specular reflection of the
illuminant in that region (see Section II-A). In implementation, the contour fitting procedure is
discretized, with finite differences serving for derivatives and summation used to instantiate
integrals and convolutions. More generally, fitting contours to images via this type of optimization
formulation is a standard machine vision technique, often referred to as active contour modeling
The Wildes et al. system performs its contour fitting in two steps. First, the image intensity
information is converted into a binary edge-map. Second, the edge points vote to instantiate
particular contour parameter values. The edgemap is recovered via gradient-based edge
detection [2], [44]. This operation consists of thresholding the magnitude of the image intensity
gradient, i.e.,
∇G( ,
x y) * I (x, y) where
∇ ≡ (∂ / x
∂ , ∂ / y
∂ ) while
2
2
2
2
( x − x0) +( r −
) / 2
G( ,
x y) = 1/ 2
σ
Πσ
ro
e −

is a two-dimensional Gaussian with center (xo,yo) and σ is standard deviation that smooths the
image to select the spatial scale of edges under consideration. In order to incorporate directional
tuning, the image intensity derivatives are weighted to favor certain ranges of orientation prior to
taking the magnitude. For example, prior to contributing to the fit of the limbic boundary contour,
the derivatives are weighted to be selective for vertical edges. The voting procedure is realized
via Hough transforms [27], [28] on parametric definitions of the iris boundary contours. In
particular, for the circular limbic or pupillary boundaries and a set of recovered edge points (xj,yj) j
= 1…..n. Hough transform is defined as




FIGURE 3: Iris and centroid detection

3.3 IRIS MATCHING
Image matching is a fundamental aspect of many problems in computer vision, including object or
scene recognition, solving for 3D structure from multiple images, stereo correspondence, and
motion tracking. This method describes image features that have many properties that make
them suitable for matching differing images of an object or scene. The features are invariant to
image scaling and rotation, and partially invariant to change in illumination and 3D camera
viewpoint. They are well localized in both the spatial and frequency domains, reducing the
probability of disruption by occlusion, clutter, or noise. Large numbers of features can be
extracted from typical images with efficient algorithms. In addition, the features are highly
distinctive, which allows a single feature to be correctly matched with high probability against a
large database of features, providing a basis for object and scene recognition. The cost of

International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (1) : 2011
4

---