1. technology and computing

International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 2
e-ISSN: 2394-8299
p-ISSN: 2394-8280
ENCODING STRATEGY FOR at-a-distance IRIS RECOGNITION
Vibha S Rao1 , P Ramesh Naidu2 ,
1
PG Student, CSE, Sri Venkateshwara college of Engineering, Bangalore, India.
Assistant Professor, CSE, Sri Venkateshwara college of Engineering, Bangalore, India.
__________________________________________________________________________________________
2
ABSTRACT
With the fast development of hardware and software in recent years, intelligent biometric
technologies, comprising automated methods for uniquely recognizing people have been widely used in the
industry in order to provide the security. Due to unique characteristics of iris texture, iris recognition is
emerging as one of the reliable promising biometric technology. The iris recognition consists of iris
localization, normalization, encoding and comparison. In this paper, reflection removal and the encoding
part of iris recognition is analysed.
Keywords : Iris features, Reflection removal, Normalization, GeoKey encoding.
--------------------------------------------------------------------------------------------------------------------------------------I. INTRODUCTION
In imaging science, image processing is any form of signal processing for which the input is an image,
such as photograph or video frame; the output of image processing may be either an image or set of
characteristics or parameters related to the image. The acquisition of images is referred to as imaging. In our
work we acquire the iris image using CASIA.V4 database.In order to provide authorized users with secure
access to information and to provide a variety of services across the internet, a reliable Identity management has
become a challenging to task. The primary task in an identity management system is the determination of an
individual‟s identity. The reliable solution to recognize the individual‟s identity is biometric technology. They
recognize the identity based on their physical and /or behavioural characteristics that are inherent to the person.
A lot of biometric techonologies like face recognition, vein matching, DNA matc hing, fingerprint recognition
etc,. We choose iris as a reliable biometric due to its advantages such as stability, availability, uniqueness, high
recognition rate and also some tests have proved that they produce no false matches in millions of comparison
tests. These characteristics make it very attractive for use as a biometric for identifying individuals.
The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. The
primary function of the iris is to control the amount of light entering through the pupil and also accounts for the
size of the pupil. As the structure of the iris remains stable throughout a person‟s life, except for direct physical
damage or changes caused by eye surgery. This makes the usage of an iris pattern to be as unique and the added
advantage is that it is an internal organ and is less susceptible to damages over a person‟s lifetime. In Fig.1.1
shows the iris anatomy. The iris anatomy is more relevant to the proposed iris recognition methodologies. Thus,
the key visible features, as annoted in Fig. 1.1, can be briefed as below.
Figure 1.1: Eye anatomy
As stop-and-stare mode operation is used in Conventional iris recognition systems requires significant
cooperation from the users. The usage of visible imaging can relax such requirement and enable iris recognition
in less cooperative environment using images acquired at further distance.
In this paper we propose a method where we exploit the localized and globalized iris feature to increase
the recognition accuracy and also the reflection removal technique. The paper is organised as follows: section II
consist of survey on how the iris encoding strategy for exploiting the iris feature.
IJRISE| www.ijrise.org|[email protected][224-228]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 2
e-ISSN: 2394-8299
p-ISSN: 2394-8280
II. LITERATURE SURVEY
A survey gives an oversight of a field and is thus distinguishing from a sort of study which consists of a
microscopic examination of a turf; it is a map rather than a detailed plan. The survey must be planned before a
start is made. Literature survey gives the preliminary information related to working area of project, it helps in
understanding the background related to the topic.
In Ref. [1] the inconsistent bits in the iris codes are estimated and are removed while computing the
matching scores. This method is suitable for close distance iris image but cannot accommodate for large image
variation like scaling and rotation for at-a-distance iris images. Ref. [2] is an extended work of [1] where each
bit in iris code are quantified by employing weighting strategy i.e., higher values are assigned to consistent bits
while lower value to the inconsistent bit. This strategy though tolerant to noise but doesn‟t overcome the
shortcomings of [1]. In Ref. [3] the iris features are recovered using the sparse coefficients which are obtained
from the dictionary. The major disadvantage is the construction of sparse dictionary for noisy iris images as
accurate estimation of sparse coefficients during the recovery process is challenging.
III.PROPOS ED WORK
The primary objective of our work is reflection removal and simultaneously exploiting globalized and
localized iris feature during the encoding part. This section includes subsection which describes the acquisition
of iris image, reflection removal, normalization, Geokey encoding and matching.
A. Acquisition of iris image
We obtain the iris image using publically available CASIA -Iris-Distance. Long-range multimodal biometric
image acquisition and recognition system (LMBS) is used to capture the iris image. An intelligent multi-camera
imaging system which contains the advanced biometric sensors is used to capture the user image from 3 meters
away. High resolution (2352*1728) camera is used to capture the iris image so that both the dual-eye iris and
face patterns are included in region of interest. Fig 3.1 is an example of image stored in the database.
Figure.3.1: An example image of CASIA-Iris-Distance.
B. Reflection Removal.
Due to the uncontrolled light source, imaging in real environment leads to illumination variation i.e.,
Specular reflection and Lighting reflections. Here we address the problem of specular reflection. Specular types
of reflection are caused while imaging under natural light. Reflection mainly occurs within the iris region and
significant portion of the iris pattern, pupil and cornea are affected. Thus results in the failure of automatic
segmentation and recognition. The below fig 3.2 illustrates the specular reflection.
Figure 3.2: An example of specular reflection
Therefore, we adopt Morphological operation used to remove reflections from image:
1. The complement of the iris image is taken to make the reflection lighter.
2. The iris image is then filled with holes to darken the reflections
3. The complement of the image is taken again to convert the image back to grayscale
I = imcomplement (imfill (imcomplement(I), 'holes'));
IJRISE| www.ijrise.org|[email protected][224-228]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 2
e-ISSN: 2394-8299
p-ISSN: 2394-8280
The result of iris image after reflection removal is illustrated in the fig 3.3.
Figure 3.3: Image after reflection removal
C. Normalization
Stretching of the iris are caused by pupil dilation from illumination variation are main reason for
dimensional inconsistencies between eye images. Varying imaging distance, rotation of the camera, head tilt,
and rotation of the eye within the eye socket are the other sources of inconsistencies. As pupil region are usually
not concentric within iris region they must also be taken into account while normalizing the iris region to a
constant radius.
Normalization is a process that changes the range of pixel intensity values i.e., usually to bring the image
into a range that is more familiar or normal. This transformation of iris region to a fixed dimension allows
comparisons of two photograph of the same iris under different condition. This is possible as both the
photograph have characteristic feature at the same spatial location. To compensate the stretching of iris texture
as pupil change in size, and have a new model of iris which removes the non-concentricity of iris and pupil, the
iris region is unwrapped into a rectangular region
In our work we employ min-max normalization technique. Normalization transforms an n -dimensional
grayscale image
{
}
{
}
With intensity values in the range (Min,Max), into a new image
{
}
{
}
With intensity values in the range (newMin, newMax).
The linear normalization of grayscale digital image is performed according to the formula
For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process entails
subtracting 50 from each of pixel intensity, making the the range 0 to 130. Then 255/130 is multiplied for each
pixel intensity, so that they range from 0 to 255. The normalized iris image is represented in the fig 3.4
Auto- normalization in image processing software typically normalizes to the full dynamic range of the
number system specified in the image file format.
Figure 3.4: Normalized iris image
IV.
ENCODING STRATEGY
Distantly acquired iris images under less constrained environment are usually influenced from image
variations i.e., Scaling and rotation. Whereas the conventional iris images are acquired under restricted
environment are encoded to match iris images in which their features are more stable because they are much
appropriate for images which are acquired from close distance. The GeoKey(Geometric Key) iris encoding
strategy simultaneously exploits both global phase encoding and localized iris feature. The block diagram of
encoding iris features is illustrated in the fig. 3.5.
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International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 2
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Figure 3.5: Block diagram of GeoKey encoding.
More accurate iris matching can be obtained by combining both global and localized iris feature where
global iris feature accounts for better encoding for less noisy iris region and at the same time localized iris
feature accounts for imaging variations. Therefore they make decisions beneficial.
Geometric key is referred as a set of coordinate-pairs, are pseudo randomly generated and is unique for
each system that is enrolled to the system. Thus they define how the localized iris features are encoded. The
GeoKey encoding mainly works on the local iris region pixel and thus more tolerant to variations as they are
benefited from intra-class features which are preserved in local iris region. Scaling and rotation changes for
localized iris region are applied on the unique key i.e., GeoKey rather on the image pixel. This provides the
added advantage of efficient and fast comparison operation on the local image patches.
The global iris texture are exploited by making use of global iris matchers such as Log -Gabor for encoding
the global iris feature.
A. Global iris feature encoding
Localized frequency information is extracted using Gabor filters. Due to their own shortcomings, log-Gabor
filters are used for coding the images. Log-Gabor filters uses a Gaussian transfer functions that are viewed on
logarithmic scale are much suitable for encoding the iris image. The main advantage of using the Log-Gabor
filter is that it better fits the statistics of natural image thus indicating the presence of high -frequency
components. LogGabor filters are constructed using equation (1).
(
(
))
(
(
))
...........(1)
The output of filter contains a cell which consists of the complex valued convolution results, of the same
size as the input image. Using this output, iris codes are formed for each pixel by assigning two elements. These
elements are indicated as 1 or 0 depending on the sign + or – of the real and imaginary part respectively.
B. Generating Geometric Key
A set of coordinate pair referred as geometric key K, of length d defines the location in an image patch of
size B × B. Inorder to generate the GeoKey of length d ≤ B2 , is given as:
K = {(x1 , x2 ) ~ i . i .G( 0 , 1/5 B)}i=1,...,d .....(2)
Where G(.) represents the Gaussian kernel indicating mean as zero and 1/5 B as standard deviation. A total of N
GeoKeys {Kd 1 , Kd 2 ,....., Kd n } are generated if N number of subjects are enrolling to the system.
Geometric transformations are applied only on the GeoKey rather on the iris image. These are applied using
the equation (3) and (4) for scaling and rotation respectively.
+ .....................(3)
Ḱs = {SKi=1,...,d }; S = *
Ḱθ = {RKi=1,..,d }; R = *
+ ......(4)
Where S and R represents the scaling matrix and rotation matrix. The main complexity lies in applying the
transformation on GeoKey as they depend on the key length which is lower than the block size.
C. Geometric Key Iris Encoding.
The localized iris features that are computed from normalized iris images are exploited on the smoothed
version of local window w of size S × S defined by GeoKey by performing the binary comparisons. The binary
feature f from image patch is computed using equation (5).
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Volume: 1 Special Issue: 2
,
e-ISSN: 2394-8299
p-ISSN: 2394-8280
.....(5)
where L(w, x1 ) denotes the filter responses from the Log-Gabor in the smoothed version of w at x1. More
reliable estimation of the iris features from localized iris region is obtained by computing the binary features on
the smoothed local window. This method is illustrated in the fig 3.6. The binary test is performed on the local
smoothing windows of size S × S centred at x1 and x2 , respectively. Therefore, the x1 and x2 represent the
average values of the filter responses computed from the respective local window w.
Figure 3.6: Illustration of the GeoKey iris encoding from an image patch of size.
D. Template matching.
Hamming distance (HD) is the sum of disagreeing bits of two iris templates divided by total no. of bits.
Hamming distance is effectively used inorder to efficiently match the encoded iris features that are in the binary
form.
HDj = ||codej gallery Fquery (W, Kj )|| / p *q
where codej gallery ϵ Bp*q denotes the binary gallery template of class j; W = [w1 , w2 , ...., wp*q]
represents the local windows. The binary query template of size p x q encoded with the K j is represented as
Fquery (W, Kj ); „ ‟ denotes the XOR operator. Image patches that are extracted from the normalized iris image
are at the interval h= b/2 inorder to employ overlapping block strategy thus accounts for block encoding.
Therefore, the width p and height q of the template are depended on the width and height of the normalized iris
image, block size B X B, sliding interval h, and the key length d of GeoKey.
The Hamming distance is matching metric employed by Daugman, and calculation of Hamming
distance taken only with bits that generated from the actual iris region. Two types of comparisons done for
testing hamming distance metric:
a) Intra-class comparison: Iris images from same subject are compared to see if the hamming distance is less
than a predetermined hamming distance threshold (match).
b) Inter-class comparison: Iris images from different subjects are compared to see if the hamming distance is
more than a predetermined hamming distance threshold (Reject).
V. CONCLUSION
The GeoKey encoding approach proposed in this paper simultaneously exploits the localized and
globalized iris feature thus owing their strength in accommodating less noisy iris images and image variation.
The preprocessing step of reflection removal also accounts for improving the recognition accuracy.
ACKNOWLEDGEMENT
We gracefully thank our college SVCE for providing us with all the necessary help and grooming up in
to be Master of Technology. I express my sincere gratitude to Dr. C. Prabhakar Reddy, Principal, SVCE, and
Dr.Suresha, HOD, Dept. Of CSE, SCVE Bangalore for providing the required facility and giving me an
opportunity to work on this topic. I also extend my sincere thanks to my guide P. Ramesh Naidu , Asst.
Professor, Dept. of CSE, SVCE Bangalore for the support and guiding me to work on this topic.
REFERENCES
[1]K. Hollingsworth, K. Bowyer and P.J. Flynn, “The best bits in an iris code,” IEEE Trans. Pattern Anal.
Mach. Intell., vol.31, 2009.
[2] W. Dong , Z. Sun and T. Tan, “Iris matching based on personalized weight map,” IEEE Trans. Pattern Anal.
Mach. Intell, vol. 33, no. 9, pp. 1744-1757, 2011.
[3]J. Pillai, V. Patel, R. Chellappa and N. Ratha, “Secure and robust iris recognition using random projections
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[4] Chun-Wei Tan, Ajay Kumar, “Efficient and Accurate at-a-distance Iris Recognition Using Geometric Key
based Iris Encoding”, IEEE Trans. Information Forensics and Security, 2014.
IJRISE| www.ijrise.org|[email protected][224-228]
International Journal of Research In Science & Engineering
Volume: 1 Special Issue: 2
e-ISSN: 2394-8299
p-ISSN: 2394-8280
[5] V. Štruc and N. Pavešić, “Performance Evaluation of Photometric Normalization Techniques for
Illumination Invariant Face Recognition,” in Advances in Face Image Analysis: Techniques and Technologies,
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[6]V. Štruc and N. Pavešić, “Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face
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[7]G. Bradski, “The OpenCV Library,” Dr. Dobb‟s Journal of Software Tools, 2000.
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BIOGRAPHY
Vibha S Rao 1 received the B.E degree from the Department of Information Science and
Engineering at Visvesvaraya Technological Univesity, Belgaum and currently pursuing
M.Tech degree in Computer Science and Engineering at Visvesvaraya Technological
Univesity, Belgaum.
P Ramesh Naidu 2 received the B.E and M.Tech degree in Computer Science and
Engineering from JNTU, Hyderabad. He is having a work experience of 8 years in
Teaching and 1year in Industry. Also Certified by Sun Microsystems for SCJP.
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