Rotation-Invariant Iris Texture Verification
Amol D. Rahulkar and R. S. Holambe
Department of Instrumentation and Control Engineering, AISSMS’ Institute of Information Technology, Pune
amolrahulkar_000@yahoo.com
Abstract: The iris recognition system mainly consists of iris
segmentation, normalization, feature extraction, and matching.
The feature extraction plays a very important role in iris
recognition system. This paper is presented the rotationally
invariant iris texture feature extraction method which uses the
DWT and DT-CWT jointly. The proposed approach divides the
normalized iris image into six sub-regions and the combined
transform is applied separately on each region to capture the
information in nine directions. The rotation invariant feature
vector is formed by estimating the energies and standard
deviation of each sub-band. The Euclidean distance is used to
match the feature vectors of template and test iris image. The
performance of iris recognition with combine approach on
UBIRIS and MMU1 database is compared with Gabor, DWT,
and DT-CWT in terms of FAR, FRR, and computational
complexity. The experimentation shows that proposed method
improves the performance.
Index Terms- Iris recognition, DWT, DT-CWT, Gabor, Biometrics.
I INTRODUCTION
Biometric authentication is an automated method for
verifying an individual. The authentication is based on the
physiological or behavioral characteristics of human being.
Among various biometric techniques such as (fingerprint,
signature, face, palm-print, palm-vein, iris etc.) iris is found to
be the most reliable and accurate trait for high secured
applications. It is because iris is unique, stable, and noninvasive.
Iris recognition is the process of identifying an
individual by analyzing its iris pattern. The iris is a muscle
within the eye that regulates the size of the pupil. It is the
colored portion of the eye with coloring based on the
pigmentation within the muscle. The basic block diagram of
iris recognition system is shown in Fig.1. The whole system
consists of segmentation, normalization, feature extraction,
and matching.
The segmentation is the separation of iris from an
eye image. In 1993, Daughman [1] pioneer of iris recognition
system developed an integro-differential operator to localize
the inner and outer boundaries of the iris. In Wildes [2], first
the image intensity information converted into binary edge
map, and then edge points vote to instantiate the particular
contour parameter values. The edge map is recovered via
gradient based edge detection with the circular Hough
Transform. Li Ma et. al. [3], [4] first project the image in the
vertical and horizontal directions to approximately estimate
the center co- ordinates of the pupil. The center co-ordinates
of the iris are estimated with the help of threshold. The canny
edge detector and Hough Transform are then used to calculate
the parameters of the iris. The normalization is used to
simplify the further processing. The feature extraction is the
heart of the iris recognition system which extracts the features
from normalized iris image. The feature extraction generally
classified into three main categories-phase based method,
zero-crossing detection and texture based method. The Gabor
wavelet, Log-Gabor wavelet are used to extract the phase
information proposed by Daughman [1], Meng and Xu [5], L.
Masek [6], Proenca and Alexandre [7], M. Vatsa et. al. [8].
Boles and Boshash [9], Sanchez–avilla [10] used the DWT to
compute the zero-crossing representation at different
resolutions of concentric circles. Wildes [2] proposed the
characterization of iris texture through Laplacian Pyramid
with four different resolutions. Many researchers extracted the
texture features using bank of spatial filter [3], Haar [11]. The
Hamming Distance, Euclidean, weighted Euclidean Distance,
and signal correlation are used for the matching purpose. The
detailed literature on iris recognition can be found in [12].
The rest of this paper is organized as follows.
Section 2 provides an overview of proposed approach. A
detail description of the iris preprocessing, feature extraction,
feature vector and matching are given in Section 3, 4, 5 and 6
respectively. Section-7 reported the experimental results.
Finally, the conclusion is given in Section 8.
2. OVERVIEW OF PROPOSED APPROACH
The two noisy iris databases we have collected, and
observed that iris consists of irregular characteristics such as
freckles, crypts, radial streaks, radial furrows, collarette, rings
etc.[1].The verification rate in the presence of noise is
improved by dividing the iris into six-regions and extracted
the features from each region using Gabor, DWT, and DTCWT separately. In order to improve the performance, we
have derived the scale and rotational invariant features from
the combination of DWT and DT-CWT which extracts the
information in nine different directions.
Fig. 1. Block diagram of Iris Recognition system.
3. PREPROCESSING
44
IRIS TEXTURE VERIFICATION
Segmentation: The iris can be segmented by detecting the
limbic boundary (between sclera and iris) and the pupillary
boundary (between pupil and iris). In this paper, we have used
Where
the integro-differential operator [1] as
max G (r ) *
( r , x 0, y 0)

I ( x, y )
ds

r x 0, y 0,r 2r
(5)
x0, y0 specify position in the image, ( ,  )
specify the effective width and length, and (u 0, v0) specify
the modulation, which has the spatial frequency
(1)
This operator searches over the image domain (x, y) for the
maximum change in the blurred partial derivative with
respective to increasing radius r of the normalized contour
integral of I(x, y) along a circular arc ds of radius r and center
coordinate(x0,y0). Gσ(r) is Gaussian filter and * is convolution
operator.
 0  u 0 2  v0 2
. In this work, Gabor is implemented
with 4 scales and 6 orientations.
Normalization : The size of the iris may change due to the
illumination variation caused pupil dilation and constriction.
Such elastic stretching or constriction of trabecular meshwork
of iris affect on the iris recognition accuracy. In order to
achieve accurate recognition we have used the Daughman’s
rubber sheet model [1] where the circular portion of iris ring
is unwrapped in anticlockwise direction into rectangular fixed
block by (2).
I (r , )  I [ x(r , ), y (r , )]
(2)
where,
x(r , )  (1  r ).xp( )  r.xl( )
(3)
(a)
(b)
(c)
y (r ,  )  (1  r ). yp( )  r. yl ( )
(4)
xp(θ), yp(θ) referred the pupillary boundary points and xl (θ),
yl (θ) denoted the limbic boundary points. The values of r lies
in [0 1] and θ locates in the range of [0 2π]. Hence, it will
become scale, translation invariant and also subsequent
processing becomes very easy.
(d)
(e)
Enhancements: The normalized image still has low intensity
contrast and may have non uniform brightness caused by the
position of light sources. Li Ma et. al. [3,4] proposed the
method divides into two steps. First is background brightness
estimation and the second is histogram equalization. We used
these two steps to obtain well distributed texture image which
suit very effective for feature extraction. The results are
shown in Fig.2.
(f)
4. FEATURE EXTRACTION
Gabor Transform: Gabor Transform is constructed by the
modulation of Gaussian function with complex sinusoidal
function. It provides the optimum conjoint localization in
space and frequency. It is because sine wave perfectly
localized in frequency, but not localized in space. So
modulation of sine wave with Gaussian provides the
localization in space. The centre frequency of the filter is
specified by the frequency of the sine wave, and the
bandwidth of the filter is specified by the width of the
Gaussian.
(g)
Fig.2. Iris Preprocessing (a).Original eye image. (b) Separated iris
region (c).Original normalized image. (d) Brightness estimation. (e).
Enhanced image.(f), (g)Partitioned image
Discrete
Wavelet
Transform


(DWT):
 
The
DWT
decomposes an image f x, y  L R
into the form of
set of dilation and translation of wavelet functions
2
2
 l x, y  and scaling function  ( x, y ) .
45
 j 0   jr0,l  j. ij 0,l ;  j 0   rj 0,l   ij 0,l
AKGEC JOURNAL OF TECHNOLOGY, vol. 1, no.1
f ( x, y )  kZ 2 s j 0, k   j 0, k ( x, y )     wlj , k  lj , k ( x, y )
l j  j 0k z
Where  j 0,k ( x, y)  2

l
j ,k
j0
(6)
  (2 ( x, y)  k ) ,
j0
( x, y)  2  (2 ( x, y)  k ) and l    {0 ,90 ,45 }
j
j
0
The
DT-CWT
{  75
2
0
0
The standard DWT is implemented with the help of
appropriately designed Quadrature Mirror filters (QMF). It
consists of low-pass coefficients g[n] and high-pass
coefficients h[n ] where n  0    L . These two impulse
responses thus related with the scaling and wavelet functions
as:
 ( x )  2   g[ n]   ( 2 x  n)
(7)
 ( x)  2   h[n]   (2 x  n)
(8)
0
gives
the
(14)
information
in
,45 ,15 ,15 ,45 ,75 } six directions.
0
0
0
0
0
It is always desirable to design the iris recognition
system must be scale, translation and rotation invariant. Our
system is achieved the scale and translation invariant by
normalizing the iris ring into fixed rectangular size. The
rotation invariance is achieved by unwrapping the iris ring at
five different initial angles -50,-100, 00, 50, 100. We thus
created the five templates which denote the five rotation
angles for individual iris.
V. FEATURE VECTOR
In this work, each iris image is decomposed up to fourth level.
The energy using L1 norm and standard deviation [16] from
nZ
nZ
The products of above 1-D wavelet and scaling functions can
be used to obtain the 2-D scaling and wavelet functions from
(9)-(12).
 (i, j )   (i )   ( j )
(9)
 1 (i, j)   (i)  ( j)
 2 (i, j)   (i)   ( j)
 3 (i, j )   (i)  ( j )
(10)
(11)
Dual Tree-Complex Wavelet Transform (DT-CWT): The real
DWT has poor directional selectivity, phase invariance, and
lacks from shift invariance property. These problems can be
solved by using Complex Wavelet Transform with
introducing limited redundancy [15]. In this, two parallel fully
decimated tree with real filter coefficients are used to achieve
Perfectly Reconstruction (PR) and good frequency response
characteristics as shown in Fig. 3(b). The DT-CWT
decomposed a signal f(x) in terms of complex shifted and
dilated mother wavelet filters  (x ) and scaling function
 (x).
lZ
 c j ,l  j ,l ( x)
(13)
j  j 0 lZ
Where s j 0 ,l is the scaling coefficient and c j ,l is the complex
wavelet coefficients with
Fig.3(b).1-D DT-CWT
(12)
The equation (9) known as approximation coefficients (LL)
and the remaining three are known as detail coefficients (LH,
HL, HH). The LH, HL, and HH sub-bands give the
information in horizontal, vertical, and diagonal direction.
Thus, the filters divide the input image into aforementioned
four non-overlapping multi-resolution sub-bands LL1, LH1,
HL1, and HH1. The sub-band LL1 represents coarse-scale
DWT coefficients while the sub-bands LH1, HL1, HH1
represent the fine scale DWT coefficients. In order to obtain
the next coarser scale of wavelet coefficients, the sub-bands
LL1 is further decomposed to get again the four sub-bands as
shown in Fig .3(a).
f ( x )   s j 0 ,l   j 0 , l ( x ) 
Fig.3(a) Sub-band decomposition
of real DWT
 j 0 and  j ,l complex as
each sub-band are computed as the texture feature vector from
(15) and (16).
Ek 
k 
M N
1
 ck (i, j)
M  N i 1 j 1
M
1

M  N i 1
(15)
N
 ck (i, j )   k (i, j )
2
(16)
j 1
Where Ek and  K are the energy and standard deviation for
the kth sub-band of dimension M  N with coefficients ck (i, j).
Hence, at each scale and direction, energy and
standard deviation based features are computed and the
resultant feature vector is given by (17)
f  f E 1 , f  1 f E 2 , f  2 f E 3 , f  3 ...
(17)


Each iris image is decomposed using Gabor, DWT, and
DT-CWT. Thus four different sets of feature vectors i.e. set1
using Gabor Transform, set2 using DWT, set3 using DTCWT, and set4 using the combination of DWT and DT-CWT
are derived.
VI. MATCHING
The Euclidean distance (18) is used as a classifier of an
unknown testing iris feature vector and is compared with a set
of known iris feature vectors.
1
 N
2
EDi    Ei  Ti  
 i 1

(18)
46
IRIS TEXTURE VERIFICATION
where N is the dimension of feature vector. Ei is ith component
of an unknown iris and Ti is ith value of known iris.
VII. EXPERIMENTAL RESULTS
The approach is implemented using MATLAB 7.0 with Intel
Dual core P4 processor, 2.0 GHz and 1GB RAM. In order to
evaluate the performance of the proposed scheme, we selected
the two databases namely UBIRIS [13] and MMU1 [14]. The
UBIRIS database consists of 1877 iris images from 241
persons captured in two different sessions using Nikon E5700
camera. The images in this dataset have artifacts in the form
of reflection, contrast, natural luminosity, focus and
eyelids/eyelashes obstructions. The MMU1 database contains
45 subjects having 5 images for left and 5 images for right eye
which includes 450 irises from 90 classes. The images in this
database are captured with LG IrisAccess camera which
contains, almost exclusively, iris obstructions by
eyelids/eyelashes and specular reflections. In order to be the
convenient for comparison purpose of both the databases, we
have selected 90 subjects randomly from UBIRIS dataset
including session 1 and session 2 contains 5 images per
person. The images selected from UBIRIS dataset for our
work are mostly under non-ideal environmental conditions.
The Daughman’s integrodifferential operator is used
to segment the iris. The Daughman’s rubber sheet model is
then used to normalize the iris ring into the rectangular fixed
size of 64x360 with five initial angles (-50,-100, 00, 50, 100) to
achieve scale, translation, and rotation invariance. Thus, the
two images each with five different angles for each person
enrolled in the database. The aim was to propose the robust
test under noisy environment; we partitioned the normalized
images into six sub regions as shown in Fig.2 (f) and (g).
Each sub region is decomposed using Gabor, DWT
(db3), DT-CWT, and DWT with DT-CWT jointly up to four
levels. The local discriminating feature vectors are derived for
each region by estimating the energy and standard deviation
of the individual sub-band. The same process repeats for test
iris. The test iris vector is compared with 10 enrolled iris
feature vectors and selected the minimum score. The total
number of inter and intra class comparisons on each subregion are 200250 and 4500 respectively. The same
experiment carried out on the whole iris normalized image to
extract the global features. Thus, we have performed total
eight experiments. The performance measured based on FAR,
FRR, and accuracy. Overall, it is observed from Table 1, 2,
and 3 that the performance of the combination of DWT with
DT-CWT better than the use of individual DWT, DT-CWT,
and Gabor. It is because Gabor is highly redundant; DWT is
suffering from limited directionality, shift invariance, and lack
of phase invariance, DT-CWT extracts the information in six
directions. Hence, the combination of DWT and DT-CWT
overcome the individual limitations and extracts the
information in nine directions with limited computational
complexity
0 ,15 ,15 ,75 ,75 ,45 ,45 ,45 ,90 .
0
0
0
0
0
0
0
0
0
7.1 Results
TABLE .1 Comparison of the Computational Complexity
Method
Gabor Transform
DWT
DT-CWT
DWT+DT-CWT
Feature extraction(ms)
848
33
97
186
TABLE 2 Comparison for UBIRIS Database.
DWT
DT-CWT
DWT+DT-CWT
(proposed)
FRR
Accuracy
(%)
(%)
Features
FAR
(%)
FRR
(%)
Accuracy
(%)
FAR
(%)
FRR
(%)
Accuracy
(%)
FAR
(%)
Global
8.44
8.49
91.53
6.56
6.67
93.385
6.62
6.44
Proposed
(local)
3.91
4.00
96.04
3.54
3.56
96.45
2.45
3.56
Gabor
FAR
(%)
FRR
(%)
93.47
7.00
7.11
96.995
2.40
2.44
Accuracy
(%)
92.95
97.77
TABLE 3 Comparison for MMU1 Database.
FAR
(%)
FRR
(%)
Accuracy
(%)
FAR
(%)
FRR
(%)
Accuracy
(%)
DWT+DT-CWT
(proposed)
FAR
FRR
Accuracy
(%)
(%)
(%)
16.01
14.44
84.77
16.31
16.89
83.40
15.21
15.31
84.74
15.66
15.66
7.25
7.33
92.71
6.77
6.89
93.17
6.18
6.89
93.46
9.11
9.33
DWT
Features
Global
Proposed
(local)
DT-CWT
Gabor
FAR
(%)
FRR
(%)
Accuracy
(%)
84.34
90.78
47
Fig. 6. (a) ROC plot: performance of the algorithms on the UBIRIS database
Fig. 6. (b) ROC plot showing the performance of the algorithms on the MMU1
database
VIII. CONCLUSION
In this paper, we have presented an effective approach for iris
feature extraction. This method is based upon the
decomposition of an iris image using DWT (db3) and DTCWT jointly which extracts the information in nine
orientations with the introduction of limited computational
complexity. Furthermore, the proposed method divides the
image into six sub-images and derives the local features
separately from each region. The experimental analysis
illustrates that suggested method reduced the FAR and FRR
by 73% and 44.72% respectively in UBIRIS database whereas
59.36%, 54.99% respectively in MMU1 database against
global features. It is also observed that proposed method
slightly improved the performance over the individual
transforms. The proposed method is scale, translational, and
rotational invariant and also performs in the presence of noise
as we have not considered any noise elimination technique
during the segmentation of an iris. It is also observed that the
computational complexity of the proposed method is 5 times
less than the Gabor Transform which suits better for the real
time applications. For future work, it is necessary to remove
the HH sub-band of DWT from DWT+DT-CWT to overcome
the redundancy and captures the information in number of
directions so as to improve the FAR and mainly FRR.
ACKNOWLEDGEMENT
The authors would like to acknowledge Dr. Nick Kingsbury who
has helped us by providing the material on DT-CWT. We take this
opportunity to express our gratitude towards Dr. P. B. Mane, Principal,
AISSMS IOIT, Pune (Ms), India for his constant encouragement and support.
IX. REFERENCES
[1]. John G. Daughman, ”High confidence of visual recognition of
persons by a test of statistical independence”, IEEE transaction on
pattern analysis and machine intelligence, vol.15, No.11, November
1993.
[2]. R. P. Wildes, ”Iris Recognition: An emerging Biometric
Technology” ,Proceedings of the IEEE,Vol.85,No.9,September
1997.
[3]. Li Ma, Tieniu Tan, Yunhong Wang, Dexin Zhang, ”Personal
identification Based On Iris Texture Analysis”, IEEE Trans. On
Pattern Analysis and Machine Intelligence, vol.25, No, 12, December
2003.
[4]. Li Ma, Tieniu Tan, Yunhong Wang, Dexin Zhang, ”Efficient Iris
Recognition By characterizing Key local variation”, IEEE Trans, On
Image processing, Vol. 13,No. 6,June 2004.
[5]. Hao Meng and Cuiping Xu, ”Iris recognition algorithm based on
gabor wavelet transform”, Proc. Of the 2006 IEEE, International
conference on mechatronics and automation, June-25-28, 2006,
Luoyang, China.
[6]. L. Masek, “Recognition of Human Iris pattern for biometric
identification,”M.Thesis, The University of Western Australia, 2003.
[7]. Hugo Proenca and Luis A.Alexandre,” Toward Noncoperative Iris
Recognition: A Classification Approach Using Multiple Signatures”,
IEEE Trans. On Pattern Analysis and Machine Intelligence, Special
Issue on Biometrics, July, 2007, vol.9, No. 4, pag.607-612.
[8]. Mayank Vatsa, Richa Singh, Afzel Noore,”Improving Iris
Recognition
Performance
Using
Segmentation,
Quality
Enhancement, Match Score Fusion, and Indexing”, IEEE Trans. On
Systems, Man, and Cybernetics-Part B: CYBENETICS, Vol. 38,
No.4, August 2008.
[9]. W. W. Boles and B.Boashash, ”A Human Identification Technique
Using Images of the Iris and Wavelet Transform”, IEEE Trans. On
Signal Processing, Vol. 46, No. 4,April 1998.
[10]. C.Sanzhez-Avila,”Iris Based Biometric Recognition Using Dyadic
Wavelet Transform”, IEEE AESS System magazine, pp.3-6,
October 2002.
[11]. Shinyoung Lim,, Kwanyong Lee, Okhwan Byeon, Taiyun Kim,
”Efficient iris recognition through improvement of feature
vector and classifier”, ETRI journal, volume 3,No.2,June2001.
49
IRIS TEXTURE VERIFICATION
[12]. Kevin W.Bowyer, Karen Hollingsworth, and Patrick J. Flynn,”Image
Understanding for Iris Biometrics: A Survey”, Compute Vision and
Image Understanding 110(2), 281-307, May 2008.
[13]. [Online] www.iris.di.ubi.pt.
[14]. [Online]
Multimedia
university
iris
database,
http://www.pesona.mmu.edu.my/~ccteo/
[15]. Ivan W. Selesnick, R. G. Baraniuk,Nick G. Kingsbury, “ The Dual
Tree Complex Wavelet Tranform”, IEEE Signal Processing
Magazine [123] November, 2005.
[16]. Manesh Kokare, P.K.Biswas, B.N.Chatterji ,” Rotation invariant
Texture Image Retrieval using Rotated complex wavelet filters”,
IEEE Trans. On Systems, man and cybernetics-Part
B:Cybernetics, Vol.36, No.6, December 2006.
Amol D. Rahulkar is currently working as
Assistant
Professor
in
Department
of
Instrumentation and Control Engineering,
AISSMS’ Institute of Information Technology,
Pune, affiliated to University of Pune. He is
working towards his Ph.D. at S.G.G.S.I.E. & T.,
SRTMU, Nanded. He received his B.E. from the
same institute and M. Tech. from IIT, Kharagpur.
He has seven years of experience in teaching
undergraduate students. His research interests are
in the areas of digital image processing,
Biometrics and Reliability Engineering.
50