IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR

Sundarapandian et al. (Eds) : CSE, CICS, DBDM, AIFL, SCOM - 2013
pp. 65–74, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3307
IRIS BIOMETRIC RECOGNITION SYSTEM
EMPLOYING CANNY OPERATOR
Binsu C. Kovoor1
, Supriya M.H.2
and K. Poulose Jacob3
1,3
Department of Computer Science, Cochin University of Science and
Technology, Cochin, Kerala, India
1
binsu.kovoor@gmail.com.,3
kpj@cusat.ac.in
2
Department of Electronics, Cochin University of Science and Technology,
Cochin, Kerala, India
supriyadoe@gmail.com
ABSTRACT
Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
KEYWORDS
Iris, Canny, Daugman, wavelet, Hamming Distance.
1. INTRODUCTION
Biometrics-based personal authentication systems have recently gained intensive research interest
due to the unreliability and inconvenience of traditional authentication systems. Biometrics
recently became a vital element of any successful person identification solutions as biometric
traits cannot be stolen, shared or even forgotten[1]. Among biometric technologies, iris-based
authentication systems bear more advantages than any other biometric technologies as it offers an
excellent recognition performance .Iris patterns are believed to be unique due to the complexity of
the underlying environmental and genetic processes that influence the generation of iris pattern.
These factors result in extraordinary textural patterns that are unique to each eye of an individual
and is even distinct between twins[2].

66 Computer Science & Information Technology (CS & IT)
Iris, a delicate circular diaphragm, lies between the cornea and the lens of the human eye. Since
the iris patterns do not alter significantly during a person‘s lifetime, it is considered to be one of
the most stable and precise personal identification biometric [1][3][4]. Iris recognition techniques
identifies a person by mathematically analyzing the unique random patterns that are visible within
the iris and making comparisons with an already existing database.
The basic block diagram of iris recognition system is as shown in Figure 1. The different stages in
the implementation of the system consist of segmentation, normalization, feature extraction and
matching.
Eye
image
Iris
Segmentation
Normalisation Feature
Extraction
Eye
image
Iris
Segmentation
Normalisation Feature
Extraction
Matching
Decision
DB
enrolment
Figure 1. Basic block diagram of iris recognition System
A prototype has been implemented and tested using UBIRIS database[5]. The prototype had a
recognition rate of 89% and rejection rate of 95%.
2. METHODOLOGY
The pre-processing stage requires the localization of the iris which generally involves the
detection of the edge of the iris as well as that of the pupil. Since varying levels of illumination
can result in dimensional inconsistencies between eye images due to the stretching of the iris,
normalization needs to be performed so that iris region is transformed to have fixed dimensions.
After unwrapping the normalized iris region into a rectangular region, it is encoded using Haar
wavelets to generate the iris code. In the recognition stage, Hamming distance is used for
comparison of the iris code, the most discriminating feature of the iris pattern, with the existing
iris templates.
2.1. Iris Localization
The different steps involved in the Iris Segmentation or localization is as shown in Figure 2. The
iris region approximated by a ring defined by the iris/sclera (limbic) boundary and the iris/pupil
(pupillary) boundary, needs isolation by the removal of the eyelids and eyelashes [6].

Computer Science & Information Technology (CS & IT) 67
Dimension
Reduction
Eye
Image
Iris
Iris
Extraction
Removal of
Eyelashes&
Eyelids
Boundary
Detection
Figure 2 Iris Segmentation Stages
2.1.1. Dimension Reduction and Iris Extraction
For the reduction of the computational complexity, the iris images are first converted into gray
scale images. For localization, the assumption that summation of the pixel values in the iris region
will be less compared to other region is used. This leads to the use of the threshold technique
based on the color of the iris. The gray level values of the iris pixels for a dark iris will be lesser
when compared to light iris. Two threshold values can be set to determine the iris region using the
histogram of the eye image. For creation of the knowledge base, eye images of both the groups
have been used so that threshold value has been computed for the extreme cases.
2.1.2. Removal of eyelids and eyelashes
Next stage is the removal of unwanted information, such as eyelashes and eyelids which can be
done using Sobel operator for detecting the edges. Sobel operator performs a 2D spatial gradient
measurement on an image and returns points where there is maximum gradient of image which
helps in edge detection as it is characterised by noticeable change in intensity. Sobel operator
consists of two 3x3 masks and which are given in equation (1) and (2) respectively.
(1)
(2)

The gradient is approximated by applying the masks to the image and computing its magnitude as
in equation (3).
(3)
To compute the gradient for the pixel of the input image I is given in equation (4).
(4)
and can be found as in equations (5) and (6). Gradient magnitude at each pixel is found
and compared with the threshold to determine, whether it’s an edge pixel or not. Sobel operator is
less sensitive to noise, due to its large convolution masks.
(5)
(6)
2.1.3. Boundary Detection
For boundary detection, the centre pixel of the eyelash removed iris image is determined and a
circular strip is extracted based on the centre co-ordinates of the pupil. For detecting the inner and
outer boundary of the iris, Integro-Differential operator [2] was used. The integro-
differential operator is defined as in equation (7).
(7)
where is the eye image, r is the radius to search for, is a Gaussian smoothing
function and s is the contour of the circle given by r,x0,y0. The operator searches for the circular
path where there is maximum change in the pixel value, by varying the radius and centre x and y
position of the circular contour. The operator is applied iteratively in order to attain precise
localization.

2.2. Iris Normalization and Unwrapping
Once the iris region has been successfully segmented from an eye image, it is transformed so that
it has fixed dimensions in order to allow comparisons. Most normalization techniques are based
on transforming iris into polar coordinates, known as unwrapping process. The normalization
process will produce iris region, which have the same constant dimensions, so that two
photographs of the same iris under different conditions will have same characteristics features.
Figure 3 shows the normalised iris image.
Figure 3. Normalized iris image
In fact homogenous rubber sheet model devised by Daugman remaps each point (x,y) within the
iris region to a pair of polar co-ordinates (r,θ), where r is on the interval (0,1) and θ is angle
(0,2π). Then the normalized iris region is unwrapped into a rectangular region. Figure 4 illustrates
the mechanism of Daugman’s rubber sheet model.
r
0 1
θ
r
θ
Figure 4. Unwrapping: Daugman’s Rubber Sheet Model
The normalized remapping of iris region from cartesian co-ordinates (x,y) to non-concentric polar
representation is modeled as proposed by [7] and is given by equation (8).
(8)
where
where is the iris region image, are the original cartesian co-ordinate, are the
corresponding normalized polar co-ordinates, and are the pupil and iris
boundary respectively along the direction.

After getting the normalized polar representation of the iris region, this region is unwrapped by
choosing a constant number of points along each radial line, irrespective of how narrow or wide
the radius is at a particular angle. Thus, a 2D array is produced with vertical dimensions of radial
resolution and horizontal dimension of angular resolution.
3. FEATURE EXTRACTION
In order to provide accurate recognition of individuals, the most discriminating information
present in an iris pattern has been extracted. Only the significant features of the iris have been
encoded so that comparison between templates is done.
In the feature extraction stage, first histogram equalization is done to enhance the iris texture in
the normalized image. After this, the canny edge detector [8] is used to extract iris texture from
the normalized image. This edge detected is a 2D image and hence to reduce the dimension of
feature it is converted into a 1D energy signal. Vertical projection is the method used for the
conversion from 2D to 1D signal. Discrete wavelet transform is applied to this 1D energy signal.
As a result a set of low frequency and high frequency coefficients are obtained. Since the high
frequency coefficients do not contain any information, it is omitted and the low frequency
coefficients each of which has a dimension of 64 bytes are taken as the iris templates. The
different steps involved in the feature extraction stage are shown in Figure 5.
3.1. Histogram Equalization
This method usually increases the local contrast of many images. Through this adjustment, the
intensities can be better distributed on the histogram. This allows for areas of lower local contrast
to gain a higher contrast without affecting the global contrast. Histogram equalization
accomplishes this by effectively spreading out the most frequent intensity values. The image
obtained after histogram equalization is shown in Figure 6. The domes in the unwrapped image
are due to the eyelid occlusion.
Figure 6. Histogram Equalised Image
Histogram
equalisation
Edge
detection
Vertical
Projection
DWTNormalized
image
Iris
Features
Figure 5. Feature Extraction Stages

3.2. Edge Detection
After histogram equalization iris texture will be enhanced in the normalized image. Edge
detection is performed to extract the iris texture from this enhanced image. Though several edge
detection techniques such as Sobel, Canny, Prewitt etc. are available, it was observed that Canny
edge detection technique is able to extract most of the iris texture from the enhanced image.
The Canny method [9] finds edges by looking for local maxima of the gradient of . The
gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to
detect strong and weak edges, and include the weak edges in the output only if they are connected
to strong edges. This method is therefore less likely than the others to be fooled by noise, and
more likely to detect true weak edges. Figure 7 shows the edge detected image using canny
operator.
Figure 7. Canny edge detected image
3.3. Vertical Projection
Vertical projection is a method used to convert the 2D signal to 1D signal. This is done to reduce
the system complexity. For vertical projection, energy of each row of the edge detected image is
calculated and is converted into a row vector. The generalized equation is shown in equation (9).
The dimension of normalized image is and is taken as . Hence, after vertical
projection its dimension is m, which is equal to 128.
(9)
3.4. Discrete Wavelet Transform
The discrete wavelet transform (DWT) decomposes the signal into mutually orthogonal set of
wavelets [10]. The DWT of signal x is calculated by passing it through a series of filters. First the
samples are passed through a low pass filter with impulse response g[n] resulting in a convolution
given in equation (10).
(10)
The signal is also decomposed simultaneously using a high pass filter h[n]. The outputs give the
detail coefficients (from the high pass filter) and approximation coefficients (from the low pass
filter) whose dimension will be 64 bytes each, since the dimension of 1D signal is 128 bytes. It is
important that two filters are related to each other and they are known as a quadrature mirror
filter. Here Haar wavelet [11] is used for wavelet transform. After wavelet transform, a set of low
frequency coefficients and high frequency coefficients each of dimension 64 bytes is obtained.
After DWT, it is observed that approximation coefficients contain information and detailed

coefficients do not have any information. Hence approximation coefficients with dimensions 64
bytes is selected as feature vector and stored in database.
4. CLASSIFICATION
In recognition stage the features of the input eye image is compared with that of the features that
is already stored in the database and if it matches, the corresponding eye image is identified
otherwise it remains unidentified. Since a bitwise comparison is necessary Hamming distance was
chosen for identification.
4.1. Hamming Distance
The Hamming distance [12] gives a measure of how many bits are the same between two bit
patterns. It is used for comparison of iris templates in the recognition stage. Hamming distance D
is given by equation (11).
(11)
where, x and y are the two bit patterns of the iris code. n indicates number of bits. Hamming
distance D gives out the number of disagreeing bits between x and y.
Ideally, the hamming distance between two iris codes generated for the same iris pattern should
be zero; however this will not happen in practice due to fact that normalization is not perfect. The
larger the hamming distances (closer to 1), the more the two patterns are different and closer this
distance to zero, the more probable the two patterns are identical. By properly choosing the
threshold upon which we make the matching decision, one can get good iris recognition results
with very low error probability.
5. RESULTS AND DISCUSSIONS
The system was tested using the UBIRIS database [5] which included 1877 images from 241
persons collected in two sessions. The images collected in the first photography session were low
noise images. On the other hand, images collected in the second session were captured under
natural luminosity factor, thus allowing reflections, different contrast levels, and luminosity and
focus problems thus making it a good model for realistic situations.
Ten sets of eye images from UBIRIS database was taken for identification. Each set consists of
three eye images of a person taken at different time. From each set a single eye image was
randomly selected and its features were stored in the database. Therefore a total of 18 images
were used for simulation. These images are called registered images since its feature is stored in
the knowledge base. The main challenge in the identification is to identify the other two images in
each set whose features are not stored. 6 images whose features are not stored in the database are
also used to test the algorithm. These images are called as unregistered images. An efficient
algorithm should identify all registered images and reject all unregistered images. Performance of
iris acceptance algorithm is validated using four parameters -False Reject (FR), False
Accept(FA), Correct Reject(CR) and Correct Accept(CA). FR is obviously the case where we
judge a pattern as not the target one while it is. FA is when the pattern is considered as the target
one while it is not. CR is when the pattern is correctly judged as being not the target one. Finally,
CA is when the pattern is correctly considered to be the targeted one. These outcomes are

illustrated in Figure 8. It was found that an optimum result is obtained at hamming distance
threshold of 0.4.
0
1
2
3
4
5
6
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
ProbabilityDensity
Hamming Distance
2
4
3
1. Correct accept rate
2. False accept rate
3. Flase rejectrate
4. Correct rejectrate
threshold
Authentics Imposters
1
Figure 8. Decision making in iris biometric system
Figure 9. Detection of iris edge using various operators
Using MATLAB, a comparison study between different classical operators, Canny, Sobel,
Prewitt, Roberts, log and zero cross was also done. The operators were applied to the enhanced
normalized image. The results, presented in Figure 9, show the performance of each of the
operators. It was found that the Canny operator outperforms the others; in fact it was the only
operator which was able to extract most of the iris texture.
6. CONCLUSIONS
Iris images were segmented and projected into 1D signal by the vertical projection and the 1D
signal features were extracted by the Haar wavelet transform. The complexity of feature
extraction method for iris recognition was low and achieved thus a considerable computational
reduction while keeping good performance.
A low dimensional feature extraction algorithm was developed and tested with 24 eye images
from the database by varying the parameters such as dimension of feature vector and hamming
distance threshold. It was found that an optimum result is obtained at hamming distance threshold
of 0.4 and feature vector dimension of 64bytes. It was also observed that canny operator was best
(a) Canny
(c)Prewitt
(e) Log
(d) Robert
(b) Sobel
(f)Zero Cross

suited to extract most of the edges to generate the iris code for comparison. Recognition rate of
89% and rejection rate of 95% was achieved.
REFERENCES
[1] Jain A.K, Ross A. and Prabhakar S.(2004) “An Introduction to Biometric Recognition”, IEEE
Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-
Based Biometrics, Vol.14, No.1, pp.4–20.
[2] Daugman J.(2004) “How iris recognition works”, IEEE Transactions on Circuits and Systems for
Video Technology, Vol.14, No.1, pp.21-30.
[3] Jain A.K, Ross A, Pankanti S,(2006) “Biometrics: A tool for information security”, IEEE Transaction
on Information Forensics and Security, Vol.1, No.2, pp.125-143.
[4] Ross A, Nandakumar K & Jain A.K,(2006) “Handbook of Multibiometrics”, Springer.
[5] Proenc H, and Alexandre L,(2004) “UBIRIS: Iris Image Database”, Available: http://iris.di.ubi.pt.
[6] Zhang D.(2003) “Detecting eyelash and reflection for accurate iris segmentation”, International
journal of Pattern Recognition and Artificial Intelligence, Vol. 1, No.6, pp.1025-1034..
[7] Jaroslav B, “Circle detection using Hough Transform,” Technical Report, University of Bristol, U.K.
Available at: http://linux.fjfi.cvut.cz/~pinus/bristol/ imageproc/ hw1/report.pdf
[8] Kovesi P, “MATLAB Function for Computer Vision and Image Analysis”, Available at:
http://www.cs. uwa. edu.au/ ~pk/Research/ MatLabFns/ index.html.
[9] Canny J,(1986) “A computational approach to edge detection”, IEEE Transaction on Pattern Analysis
and Machine Intelligence, Vol. 8, pp. 679-698.
[10] Boles W, and Boashash B,(1998), “A Human Identification Technique using Images of the Iris and
Wavelet Transform”, IEEE Trans. Signal Processing, Vol. 46, No. 4, pp. 1185-1188.
[11] Lim S, Lee K., Byeon O, and Kim T,(2001) “Efficient iris recognition through improvement of
feature vector and classifier”, ETRI Journal, Vol. 23, No. 2, pp. 61-70.
[12] Daugman J,(1993) “High confidence Visual Recognition of Person by a test of statistical
Independence”, IEEE transactions on pattern analysis and machine intelligence, Vol.15 No.11, pp.
1148-1161.
Authors
Binsu C. Kovoor is working as Assistant Professor in Information Technology, Cochin
University of Science and Technology 2000 onwards. Her areas of interest include
biometric security system, pattern recognition, database systems, data mining, data
structures, streaming audio and video signals etc. She is a Life member of ISTE and IE.
Dr. Supriya M. H joined as a faculty in the Department of Electronics, Cochin
University of Science & Technology in 1999. Her fields of interests are Target
Identification, Signal Processing, Bioinfomatics, Steganography and Computer
Technology. She has presented papers in several International Conferences in Europe,
USA, and France. She is actively involved in Research and Development activities in
Ocean Electronics and related areas and has a patent and about 87 research publications
to her credit. She is a Life member of IETE and ISTE.
Dr. K. Poulose Jacob, Professor of Computer Science at Cochin University of Science
and Technology since 1994, is currently Director of the School of Computer Science
Studies. His research interests are in Information Systems Engineering, I ntelligent
Architectures and Networks.He has more than 90 research publications to his credit. He
has presented papers in several International Conferences in Europe, USA, UK,
Australia and other countries. Dr. K. Poulose Jacob is a Professional member of the
ACM (Association for Computing Machinery) and a Life Member of the Computer
Society of India.

IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR (20)

More from csitconf (9)

Recently uploaded (20)

IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR