High Security Human Recognition System using Iris Images

ACEEE International Journal on Signal and Image Processing Vol 1, No. 1, Jan 2010

High Security Human Recognition System using
Iris Images
C. R. Prashanth1, Shashikumar D.R.2, K. B. Raja3, K. R. Venugopal3, L. M. Patnaik4
1
Department of Electronics and Communication Engineering, Vemana Institute of Technology, Bangalore, India
2
Department of Computer Science and Engineering, Cambridge Institute of Technology, Bangalore, India
3
Department of Computer Science and Engineering, University Visvesvaraya College of Engineering,
Bangalore University, Bangalore 560 001, India
4
Vice Chancellor, Defence Institute of Advanced Technology, Pune, India
prashanthcr_ujjani@yahoo.com

Abstract—In this paper, efficient biometric security after two or three years. (ii) The human Iris might be as
technique for Integer Wavelet Transform based Human distinct as the Finger Prints for the different individuals.
Recognition System (IWTHRS) using Iris images (iii) The forming of Iris depends on the initial
verification is described. Human Recognition using Iris environment of the Embryo and hence the Iris Texture
images is one of the most secure and authentic among the Pattern does not correlate with genetic determination. (iv)
other biometrics. The Iris and Pupil boundaries of an Eye
are identified by Integro-Differential Operator. The features
Even the left and the right Irises of the same person are
of the normalized Iris are extracted using Integer Wavelet unique. (v) It is almost impossible to modify the Iris
Transform and Discrete Wavelet Transform. The Hamming structure by surgery. (vi) The Iris Recognition is non-
Distance is used for matching of two Iris feature vectors. It invasive. (vii) It has about 245 degrees of freedom.
is observed that the values of FAR, FRR, EER and Iris is the only internal organ which can be seen
computation time required are improved in the case of outside the body. The probability of uniqueness among
Integer Wavelet Transform based Human Recognition all humans has made Iris Recognition a reliable and
System as compared to Discrete Wavelet Transform based efficient Human Recognition Technique. An Iris
Human Recognition System (DWTHRS). biometric system can be utilized in two contexts:
verification and identification. Verification is a one-to-
Index Terms—Human Recognition, Biometrics, Integer
Wavelet Transforms, Iris Image, High Security.
one match in which the biometric system tries to verify a
person’s identity by comparing the distance between test
Iris and the corresponding Iris in the database, with a
I. INTRODUCTION
predefined threshold. If the computed distance is smaller
Biometric solutions address the security issues than the predefined threshold, the subject is accepted as
associated with traditional method of Human Recognition being genuine, else the subject is rejected. Identification
based on personal identification number (PIN), identity is a one-to-many match in which the system compares the
card, secrete password etc., and the traditional methods test Iris with all the Irises in the database and chooses the
face severe problems such as loss of identity cards and sample with the minimum computed distance i e.,
forgetting/ guessing the passwords. Biometric measures greatest similarity as the identified result. If the test Iris
based on physiological or behavioral characteristics are and the selected database Iris are from the same subject, it
unique to an individual and have the ability to reliably is a correct match. The term authentication is often used
distinguish between genuine person and an imposter. The as a synonym for verification.
physiological characteristics include Iris, Finger Print, The Iris Verification system can be split into four
Retinal, Palm Prints, Hand Geometry, Ear, Face and stages: data acquisition, segmentation, encoding and
DNA, while the behavioral characteristics include matching. The data acquisition step captures the Iris
Handwriting, Signature, Body Odor, Gait, Gesture and images using Infra-Red (IR) illumination. The Iris
Thermal Emission of Human Body. Segmentation step localizes the Iris region in the image.
The biometric systems based on behavioral For most algorithms and assuming near-frontal
characteristics fail in many cases as the characteristics presentation of the Pupil, the Iris boundaries are modeled
can easily be learnt and changed by practice. Some of the as two circles, which are not necessarily concentric. The
techniques based on physiological characteristics such as inner circle is the pupillary boundary between the Pupil
Face Recognition, Finger Prints and Hand Geometry also and the Iris whereas the outer circle is the limbic
fail when used over a long time as they may change due boundary between the Iris and the Sclera. The noise due
to ageing or cuts and burns. Among all the biometric to Eyelid occlusions, Eyelash occlusions, Specular
techniques Iris Recognition has drawn a lot of interest in highlights and Shadows are eliminated using
Pattern Recognition and Machine Learning research area segmentation. Most segmentation algorithms are gradient
because of the advantages viz., (i) The Iris formation based that is segmentation is performed by finding the
starts in the third month of gestation period and is largely Pupil-Iris edge and the Iris-Sclera edge. The encoding
complete by the eighth month and then it does not change stage encodes the Iris image texture into a bit vector code.
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The corresponding matching stage calculates the distance Chin et al., [5] proposed the use of an S-Iris encoding
between Iris codes, and decides whether it is a match in which is generated from the inner product of the output
the verification context or recognizes the submitted Iris from a 1D Log Gabor filter and secret pseudorandom
from the subjects in the database. Biometrics is widely numbers. In the segmentation stage, first an edge map is
used in many applications such as access control to generated using a Canny edge detector. A Circular Hough
secure facilities, verification of financial transactions, Transform is used to obtain the Iris boundaries. Linear
welfare fraud protection, law enforcement, and Hough Transform is used in excluding the Eyelid and
immigration status checking when entering a country. Eyelash noises. The isolated Iris part is unwrapped into a
Contribution: In this paper, we propose a novel rectangle with a resolution of 20 * 240 using Daugman’s
technique for human identity authentication by Iris rubber sheet model. In matching, Hamming Distance is
Verification. We use Integro-Differential equation for Iris used to indicate the dissimilarity between a pair of Iris
localization and Daugman’s rubber-sheet model for codes.
normalization. Integer Wavelet Transformation is used to Ya-Ping Huang et al., [6] proposed a recognition
extract the features from the normalized Iris image. method which constructs basic functions for training set
Matching between the test image and the database images by Independent Component Analysis, which determines
is done using Hamming Distance. the centre of each class by competitive learning
Organization of the paper: The rest of the paper is mechanism and finally recognizes the pattern based on
organized as follows. In section II, we discuss about Euclidean Distance. No restriction for image capture
literature survey. In section III we present the Iris based owing to representation of size and rotation invariance.
Human Recognition model. In section IV we discuss the However, the algorithm uses all patterns of each class as
IWTHRS algorithm. The performance analysis presented a whole to estimate ICA basic function and when a new
in section V and concluded in section VI. class is added all the patterns must be trained again.
Schmid et al., [7] proposed an algorithm to predict the
II. LITERATURE SURVEY Iris Biometrics system performance on a larger dataset
based on the Gaussian Model constructed from a smaller
Daugman’s Algorithm [1, 2] proposed the Iris model
dataset. In the matching stage, it uses a sequence of K Iris
as two circles between the Pupil and Sclera boundaries,
codes to represent an Iris subject. The distance between a
which are not necessarily concentric. Each circle is
pair of Iris subjects is defined as a K-dimensional
defined by three parameters (xo, yo, r), where (xo, yo)
Hamming Distance, modeled as Gaussian Distribution.
locates the center of the circle of radius r. An Integro-
Fancourt et al., [8] discussed the problem of Iris
Differential Operator is used to estimate the three
Recognition using images acquired up to 10 meters away.
parameter values for each circular boundary. The
The pictures are captured with the aid of a telescope. The
segmented Iris image is normalized and converted from
manual Iris segmentation is used as a bootstrap to the
Cartesian image coordinates to polar image coordinates.
automatic segmentation. The similarity between the
The 2D Gabor filter is used to encode the Iris image to a
gallery image and probe image is measured by the
binary code of 256 bytes in length. Hamming Distance is
average correlation coefficient over sub-blocks with a
used to verify the similarity of two Iris codes.
size of 12*12 pixels. The algorithm is tested on two iris
In an algorithm proposed by Ma et al., [3], the Iris
databases with no subjects in common.
images are projected to the vertical and horizontal
directions to estimate the center of the Pupil, to save time
III. IWTHRS MODEL
in searching for the Iris boundaries. The region of Iris is
constrained close to the Pupil, because Iris texture is In this section, IWTHRS model is discussed. Figure 1
claimed to be more abundant and also it reduces Eyelid shows the block diagram of Integer Wavelet Transform
and Eyelash noise. The representation of the Iris is a based Human Recognition System (IWTHRS), which
feature vector of length 1,536 bits. A Fisher Linear verifies the authenticity of given Iris of a person. The Eye
Discriminant is used to reduce the dimension of the Iris images for study are taken from the CASIA database. The
feature vector. Integro-Differential Operator (IDO) is used for Iris
Kong and Zhang [4] proposed an Eyelash and localization and Daugman’s rubber-sheet model for
reflection segmentation in their algorithm. The Iris normalization. Integer Wavelet Transformation is used to
segmentation is implemented by using curve fitting extract the features from the normalized Iris image.
approaches. The Eyelashes are sub-classified as separable Matching between the test Iris and the database Irises is
Eyelashes and multiple Eyelashes. The separable done using Hamming Distance.
Eyelashes are segmented using a Gabor filter and the
A. Integro-Differential Operator for Image Segmentation
multiple Eyelashes are segmented by comparing the
variance of intensity values of a given area with the The Integro-Differential Operator is defined by the
predefined threshold. Four types of 1-D wavelets viz., Equation 1.
Mexican hat, Haar, Shannon and Gabor are used to
∂ I ( x, y )
max (r , xo , yo ) = Gσ (r ) ∗
extract the Iris features. In matching, the dissimilarity
between a pair of Iris codes is defined by L1 norm. ∫, y 2πr ds
∂r r , x0 0
(1)

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Where I(x,y) is the Eye image, r is the radius, Gσ(r) is from 0 to 1 and θ is angle in the interval from 0 to 2 π .
a Gaussian smoothing function, and s is the contour of The remapping of the Iris region from ( x, y ) Cartesian
the circle given by (r, x0, y0). The operator searches for
coordinates to the normalized non-concentric polar
the circular path where there is maximum change in pixel
representation is modeled as given by the Equations 2, 3
values, by varying the radius and centre x and y position
and 4.
of the circular contour.
I ( x(r ,θ ), y (r ,θ )) = I (r ,θ ) (2)
Eye Image With

x(r ,θ ) = (1 − r )x p (θ ) + rxl (θ ) (3)
Integro-Differential Operator
y (r ,θ ) = (1 − r ) y p (θ ) + ryl (θ ) (4)

Daugman’s Rubber sheet model
where I ( x, y ) is the Iris image, ( x, y ) are the original
Cartesian coordinates, (r ,θ ) are the corresponding
normalized polar coordinates, and are the coordinates of
Image Enhancement the pupil and iris boundaries along the θ direction as
shown in Figure 3.

Feature Extraction using IWT

Database Hamming Distance

Figure 3. Daugman’s Rubber Sheet Model
Verified Iris
The rubber sheet model takes into account Pupil
dilation and size inconsistencies in order to produce a
Figure 1. Block diagram of IWTHRS normalized representation with constant dimensions. The
Iris region is modeled as a flexible rubber sheet anchored
The IDO is applied iteratively with the amount of
at the Iris boundary with the Pupil centre as the reference
smoothing progressively reduced in order to attain precise
point. The segmented Iris image is normalized to a size
localization and also Eyelids are localized with the path
60 * 250.
of contour integration changed from circular to an arc.
The Integro-Differential can be seen as a variation of the C. Image Enhancement
Hough Transform, as it makes use of first derivatives of In order to obtain best features for Iris verification,
the image and performs a search to find geometric polar transformed image is enhanced using contrast-
parameters. The IDO works with raw derivative limited adaptive histogram equalization [9]. The results
information and hence it does not suffer from the of image before and after enhancement are shown in
threshold problems of Hough Transform. The segmented Figure 4.
Iris image is shown in Figure 2.

(a)

(b)
Figure 4. (a) Normalized Iris before enhancement.
Figure 2. Segmented Iris with occluding Eyelids and Eyelashes made (b) Normalized Iris after enhancement.
black
D. Feature Extraction
B. Daugman’s Rubber Sheet Model
Feature extraction is the most important step in Iris
The homogenous rubber sheet model devised by
Verification. We use Haar Integer Wavelet
Daugman remaps each point within the Iris region to a
Transformation to extract the features from the
pair of polar coordinates (r ,θ ) where r is in the interval normalized Iris image. The normalized Iris image of size
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60*250 is subjected to Integer Wavelet Transformation to totally different. If two patterns are derived from the
get Approximation band, Horizontal band, Vertical band same Iris, the Hamming Distance between them is close
and Diagonal band. The Horizontal Detail band obtained to zero, since they are highly correlated and the bits
after the first level Integer Wavelet Transformation is should agree between the two Iris codes. However,
further subjected to two levels of decomposition. The because of the presence of noise due to Eyelid and
approximation band obtained after the third level Eyelashes occlusion, the Hamming Distance may vary up
decomposition consists of the prominent features. The to 0.4 even for the same Iris images captured at different
horizontal band is selected at the first two stages of instances. To increase the efficiency, we compare the Iris
decomposition, because the normalized Iris image shows image under test with all the 7 images of each group and
more details in the horizontal direction i e., angular the mean value of the 7 Hamming Distances is used to
dimensions of the actual Iris image compared to the decide whether the Iris image under test belongs to the
vertical direction i e., the radial dimension of the actual same group or not. If the average Hamming Distance
Iris image. The two dimensional approximation band obtained is greater than 0.39 then the subject is rejected
containing the prominent features is converted into a one and if the average Hamming Distance is lesser than 0.39
dimensional array and it is binarized. To binarize, we then the subject is accepted as genuine.
equate all the positive features to 1 and the negative
features to 0. This finally results in a feature vector of IV. ALGORITHM
size 256 bits. The conceptual model for the three levels
Table 1 shows the Human Identification by IWTHRS
Integer Wavelet decomposition for feature extraction is
algorithm in which the authenticity of the test Iris image
shown in Figure 5.
is verified.
Problem definition:
LL LL HL Consider an Eye image of a subject whose identity has to
LH HH be verified. The objective is to
LL i) Segment the Iris with minimum noise, ii) Normalize the
Iris, iii) Generate a minimum length feature vector, which
LH HH includes all the distinct features of the Iris, and iv) verify
the authenticity of the subject.
Assumptions:
LH HH i) The Eye image is captured using IR photography
ii) The Eye image is a gray-scale image of size 150* 200

TABLE 1. IWTHRS ALGORITHM

Input : Test Eye image.
Figure 5. Conceptual diagram for 3 levels 2D Integer Wavelet
Decomposition. Output: Verified Iris.
E. Matching i. Segment the Iris image using IDO
ii. Normalize the segmented Iris image from
Matching between the two Iris feature vectors is done
Cartesian coordinates to the normalized
using Hamming Distance. It is a measure of how many
non-concentric polar representation of size
bits are the same between two bit patterns. Using the
60*250 using Daugman’s rubber sheet
Hamming Distance of two bit patterns, a decision is made
model
as to whether the two patterns were generated from
iii. Enhance the image using contrast limited
different Irises or from the same one. In comparing the bit
adaptive histogram equalization
patterns X and Y, the Hamming Distance HD, is defined
iv. Apply Integer Wavelet Transformation to
as the sum of disagreeing bits over N, the total number of
the normalized Iris image
bits in the feature vectors and is given by the Equation 5.
v. Subject the horizontal detail band obtained
1 N in step 4 to two level IWT
HD = ∑ X j ⊕ Yj
N j =1
(5) vi. Convert the approximation band obtained
in step 5 into single dimension
vii. Binarize the one dimensional array
Since an individual Iris region contains features with viii. Find the Hamming Distance between the
high degrees of freedom, each Iris region produces a bit- binarized feature vectors obtained in step7
pattern which is independent to that produced by another with the corresponding feature vector in
Iris. On the other hand, two Iris codes produced from the the database
same Iris will be highly correlated. In ideal case, if two ix. If HD<0.39, the subject is accepted as
bits patterns are completely independent, such as Iris genuine, else rejected
templates generated from different Irises, the Hamming
Distance between the two patterns is high. This occurs
because independence implies the two bit patterns will be

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V. PERFORMANCE ANALYSIS
We tested the IWTHRS model on the CASIA Iris
image database–version 1.0, which contains 756 gray-
scale Eye images with 108 unique Eyes or classes and 7
different images of each unique Eye. The algorithm is
simulated on MATLAB 7.4 version. For the performance
analysis, we considered 200 gray-scale Iris images out of
available 756 Iris images. Table 2 gives the value of
FAR, FRR and EER obtained with Hamming Distance
threshold of 0.39 for IWT and DWT. It is observed that
the value of FAR, FRR and EER are less in the case of
Iris verification by IWTHRS compared that by Figure 6. Graph of FAR and FRR for DWTHRS
DWTHRS.

TABLE 2. COMPARISON OF FAR, FRR AND EER VALUES FOR IWTHRS
AND DWTHRS.

IWTHRS DWTHRS
FAR 0.11 0.19
FRR 0.105 0.135
EER 0.107 0.165
Table 3 gives the average computation time for
different steps viz., Segmentation, Normalization,
Enhancement, Feature extraction and Matching involved
in IWTHRS and DWTHRS. It is observed that the time Figure 7. Graph of FAR and FRR for IWTHRS
required for feature extraction in case of IWTHRS is only
0.16 ms when compared to 0.49 ms for DWTHRS. Thus VI. CONCLUSION
IWTHRS reduces the computation time for feature
extraction by 66%. This shows that the proposed system A novel technique for Human Recognition using Iris
can perform better in real time. verification has been proposed. The scheme uses the
Integer Wavelet Transformation on the normalized Iris
TABLE 3. AVERAGE COMPUTATION TIME OF THE IWTHRS AND image to extract the distinct features of the Iris. The
DWTHRS MODELS. implementation of IWTHRS in place of DWTHRS has
remarkably improved the computation speed and
IWTHRS DWTHRS
efficiency. Matching the test image with a set of seven
Time % of Time % of
images instead of only one image and finding the mean of
(ms) total (ms) total
the Hamming Distances for decision making further
time time
improves the efficiency of the algorithm. Improvements
Segmentation 12.92 94.7 12.92 92.48
can further be made by including Iris de-noising
2
techniques into the algorithm.
Normalization 0.39 2.88 0.39 2.79
Enhancement 0.08 0.58 0.08 0.57 ACKNOWLEDGMENT
Feature 0.16 1.17 0.49 3.50
1
Extraction The author is thankful to KRJS management, the
Matching 0.09 0.65 0.09 0.064 Principal, Vemana Institute of Technology, and the
Total Time 13.64 100 13.97 100 Principal, UVCE for providing the infrastructural
facilities to carry out the research work.
Figures 6 and 7 show the graph of FAR and FRR
obtained for different values of Hamming Distance REFERENCES
threshold to compare the performance of Iris based
[1] J .Daugman, “High Confidence Visual Recognition of
Human Recognition System using DWT and IWT for
Persons by a Test of Statistical Independence,” IEEE
feature extraction. As Hamming Distance increases, the Transactions on Pattern Analysis and Machine
value of FRR decreases whereas FAR increases. The Intelligence, vol.15, no. 11, pp. 1148–1161, November
value of EER is a point where FAR is equal to FRR. 1993.
[2] J. Daugman, “Statistical Richness of Visual Phase
Information: Update on Recognizing Persons by Iris
Patterns,” International Journal of Computer Vision, vol.
45, no. 1, pp. 25–38, 2001.
[3] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal
Identification based on Iris Texture Analysis,” IEEE
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Transactions on Pattern Analysis and Machine Bangalore. He obtained his BE and ME in Electronics and
Intelligence, vol. 25, no. 12, pp. 1519–1533, December Communication Engineering from University Visvesvaraya
2003. College of Engineering, Bangalore. He was awarded Ph.D. in
[4] W. Kong and D. Zhang, “Detecting Eyelash and Reflection Computer Science and Engineering from Bangalore University.
for Accurate Iris Segmentation,” International Journal of He has over 35 research publications in refereed International
Pattern Recognition and Artificial Intelligence, pp. 1025– Journals and Conference Proceedings. His research interests
1034, 2003. include Image Processing, Biometrics, VLSI Signal Processing,
[5] C. Chin, A. Jin, and D. Ling, “High Security Iris computer networks.
Verification System based on Random Secret Integration,”
Proceedings of International conference on Computer
Vision and Image Understanding, vol. 2, pp. 169-177, May
2005. K R Venugopal is currently the Principal and Dean, Faculty of
[6] Ya-Ping Huang, Si-Wel Luo and En-Yi Chen, “An Engineering, University Visvesvaraya College of Engineering,
Efficient Iris Recognition System,” Proceedings of the Bangalore University, Bangalore. He obtained his Bachelor of
First International Conference on Machine Learning and Engineering from University Visvesvaraya College of
Cybernetics, pp. 450-454, November 2002. Engineering. He received his Masters
[7] N. Schmid, M. Ketkar, H. Singh, and B. Cukic, degree in Computer Science and
“Performance Analysis of Iris-based Identification System Automation from Indian Institute of
at the Matching Score Level,” IEEE Transactions on Science, Bangalore. He was awarded
Information Forensics and Security, vol. 1, no. 2, pp. 154- Ph.D. in Economics from Bangalore
168, 2006. University and Ph.D. in Computer Science
[8] C. Fancourt, L. Bogoni, K. Hanna, Y. Guo, R. Wildes, N. from Indian Institute of Technology,
Takahashi, and U. Jain, “Iris Recognition at a Distance,” Madras. He has a distinguished academic career and has degrees
Proceedings of International Conference on Audio and in Electronics, Economics, Law, Business Finance, Public
Video based Biometric Person Authentication, pp. 1–13, Relations, Communications, Industrial Relations, Computer
2005. Science and Journalism. He has authored 27 books on Computer
[9] Karel Zuiderveld, “Contrast Limited Adaptive Histogram Science and Economics, which include Petrodollar and the
Equalization,” Proceedings of the First International World Economy, C Aptitude, Mastering C, Microprocessor
Conference on Visualization in Biomedical Computing, pp. Programming, Mastering C++ etc. He has been serving as the
337-345, May 1990. Professor and Chairman, Department of Computer Science and
Engineering, University Visvesvaraya College of Engineering,
Prashanth C R received the BE degree Bangalore University, Bangalore. During his three decades of
in Electronics and the ME degree in service at UVCE he has over 200 research papers to his credit.
Digital Communication from His research interests include computer networks, parallel and
Bangalore University, Bangalore. He distributed systems, digital signal processing and data mining.
is pursuing his Ph.D. in Computer
Science and Engineering of Bangalore L M Patnaik is the Vice Chancellor, Defence Institute of
University under the guidance of Dr. Advanced Technology (Deemed
K. B. Raja, Assistant Professor, Department of Electronics and University), Pune, India. During the past
Communication Engineering, University Visvesvaraya College 35 years of his service at the Indian
of Engineering. He is currently an Assistant Professor, Dept. of Institute of Science, Bangalore, He has
Electronics and Communication Engineering, Vemana Institute over 500 research publications in refereed
of Technology, Bangalore. His research interests include International Journals and Conference
Computer Vision, Pattern Recognition, Biometrics, and Proceedings. He is a Fellow of all the four
Communication Engineering. He is a life member of Indian leading Science and Engineering
Society for Technical Education, New Delhi. Academies in India; Fellow of the IEEE and the Academy of
Science for the Developing World. He has received twenty
Shashikumar D R is a Professor in the national and international awards; notable among them is the
department of Computer Science and IEEE Technical Achievement Award for his significant
Engineering, Cambridge Institute of contributions to high performance computing and soft
Technology, Bangalore. He obtained his computing. His areas of research interest have been parallel and
B.E. Degree in Electronics and distributed computing, mobile computing, CAD for VLSI
Communications Engineering from circuits, soft computing, and computational neuroscience.
Mysore University, Mysore and Masters
degree in Electronics from Bangalore University, Bangalore. He
is pursuing research in the area of Biometric applications. His
area of interest is in the field of Digital Image Processing,
Microprocessors, Embedded systems, Networks and Biometrics.
He is life member of Indian Society for Technical Education,
New Delhi.

K B Raja is an Assistant Professor,
Dept. of Electronics and
Communication Engineering,
University Visvesvaraya college of
Engineering, Bangalore University,
31
© 2010 ACEEE
DOI: 01.ijsip.01.01.06

High Security Human Recognition System using Iris Images

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