hybrid interpolation super resolution based enhancement of iris images - ubiquitous computing and...
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HYBRID INTERPOLATION SUPER RESOLUTION BASED
ENHANCEMENT OF IRIS IMAGES Hassan Aftab, Asif Butt, Umer Munir, Sheryar Malik, Omer Saeed
National University of Sciences and Technology, Pakistan
ABSTRACT
Iris recognition is a method of biometric authentication that uses pattern
recognition techniques based on high-resolution images of the iris. In this paper we
propose a method to obtain high resolution iris image from low resolution image
for facilitating the recognition process using hybrid interpolation super-resolution
technique that switches between New Covariance based interpolation and
Curvature based interpolation to produce sharp and refined iris images. The results
show the visually improved quality of Iris for recognition.
Keywords: Hybrid Interpolation, Super Resolution, Iris, Edges.
1 INTRODUCTION
Biometrics is the automated use of physiologicalor behavioural characteristics to determine or verify
identity. A distinction may be drawn between an
individual and an identity; the individual is singular,
but he may have more than one identity, for example
ten registered fingerprints are viewed as ten different
identities. Iris’ are composed before birth and, except
in the event of an injury to the eyeball, remainunchanged throughout an individual’s lifetime. Iris-
scan technology has been established as one of the
biometrics that is very resistant to false matching and
fraud. The false acceptance rate for iris recognition
systems is 1 in 1.2 million, statistically better than
the average fingerprint recognition system. The real
benefit is in the false-rejection rate, a measure of
authenticated users who are rejected [1]. For
localizing the Iris and performing segmentation to
extract Iris pattern a high resolution iris image isessential. Interpolation which is sometimes called re-
sampling is an imaging method to increase the
number of pixels in a digital image. Imageinterpolation addresses the problem of generating a
high-resolution image from its low-resolution
version. There are many methods for imageinterpolation and commonly used ones in medical
field include nearest neighbour, bilinear, bicubic and
cubic-spline [2], [3].
The related work on enhancement of iris images
is done by [4] which predicts the prior relation
between iris feature information of different bandsand incorporates this prior into the process of iris
image enhancement. The hybrid image interpolation
super resolution algorithm developed in Matlab was
employed on the iris’ image taken from Chineseacademy of sciences (CASIA). It yielded superior
quality image for processing, thereby facilitating the
use of iris recognition process. The proposed method
is a fast hybrid approach for enhancement of irisimages. The hybrid approach is an improvement of
method proposed by [5], [6]. In previous work, the
similar algorithm was employed on aerial images
[10]. The present work shows the effectiveness of the
algorithm to obtain high resolution iris images for
the iris recognition system. Moreover cropping
fused with hybrid image interpolation algorithmdeveloped in Matlab gives the flexibility of
extracting iris from the face of an individual for
identification or surveillance. The results proved the
potential of the image for being utilized in iris
recognition systems for enhancing e-security and
achieving maximum level of infallible security
measure. This paper is organized as follows: Section
II presents the proposed algorithm. Section III
presents the experimental results of our proposed
technique. The paper is concluded in Section IV.
2 PROPOSED ALGORITHM
Edge detection is an important parameter in iris
recognition system. The proposed algorithm
differentiates between edge points and smooth areasusing the four neighbors of new interpolated points.
The difference of maximum and minimum values of
all four points when compared with a pre-defined
threshold determines the presence of an edge or
smooth area. If the difference exceeds the threshold
it is considered an edge otherwise the point belongsto smooth area. Edges are determined using the new
covariance based method, whereas the smooth areas
are handled by the iterative curvature based approach.
The detail of the algorithm is explained in theensuing paragraphs.
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2.1 Interpolation Scheme
The interpolation scheme of proposed approach
is shown in Fig. 1. In this technique a low resolution
image is taken and padded with zeros. Next bilinear
interpolation is performed at the four corners of
padded image. Now the four diagonal neighbors of few points are available, therefore new data points
are calculated using these diagonal neighbors. In the
end the remaining data points are determined using
four horizontal and vertical neighbors to produce a
Super Resolved image.
2.2 New Covariance Interpolation New covariance based interpolation has been
used for interpolation in edges. Covariance is
employed by making a circular mask around every
unknown high resolution pixel and estimating the
high resolution covariance coefficients from knownlow resolution covariance. The process is
computationally complex as it keeps on looking for
edges in that circular mask around the high
resolution pixel until optimal MMSE (minimum
mean square error) is not achieved. The new
covariance based interpolation employees the
geometric duality property between the low-resolution covariance of four neighbour pixels and
the high resolution pixel. Geometric duality actually
couples pair of pixels at different resolution but in
same orientation. Since four near pixels are used we
have the fourth order linear interpolation equation [6]
given as:
where ‘Y’ is the required interpolated point inhigh resolution image, ‘α’ is the high resolution
covariance coefficients and ‘Y'’ is neighbouringpixels to interpolated point at low resolution image.
The neighbouring pixels of interpolated point are
known, so the only thing required is the high
resolution covariance coefficients [6].
(3)
(4)
(5)
(6)
where [C1, C2, C3, C4] are the four
neighbouring low resolution pixels value, [a1, a2, a3,
a4, b1, b2, b3, b4, d1, d2, d3, d4, e1, e2, e3, e4] are
the four neighbouring pixels of low resolution pixels
[C1, C2, C3, C4] and [w, x, y, z] are the high
resolution coefficients. Using the concept of four
equations and four unknowns the high resolution
coefficients are estimated [6].
(7)
where ‘c5’ is the high r esolution pixel value. Fig.
2 shows the complete process of new covariance
based interpolation [6].
2.3 Curvature Interpolation For interpolation in smooth areas curvature
based method [7] has been employed. Curvature
based interpolation performs bilinear interpolation
along the direction where second derivative is lower.It is followed by iterative refinement of the
interpolated point following the isophote curve.
Isophote curve is an intensity level curve which
compares present value with previous and next value
and change the intensity of the present value. In this
way the linear curve is changed into isophote curve
[8]. In case of diagonals, it finds the difference of
intensity levels between diagonals at oppositedirection and performs bilinear interpolation, where
the difference is less. The differences ‘V1’ and ‘V2’
in two different directions are presented in [7], [8].
where ‘P’, ‘Q’, ‘P'’ and ‘Q'’ are intensity values
of the four neighbor points of interpolated pixel. The
direction in which second derivative is lower,
bilinear interpolation is performed [7] as given in
equation 10 and 11.
where ‘P1’ and ‘P2’ are interpolated points in
two different directions. Next, iterative refinements
are carried out on the interpolated points to follow
the isophote curve. Fig. 3 shows the complete
process of iterative curvature based interpolation [7].
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Figure 1: Illustration Interpolation scheme of proposed method
Figure 2: New Covariance based Interpolation Scheme
Figure 3: Iterative Curvature based Interpolation Scheme
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3 EXPERIMENTAL RESULTS
The proposed algorithm is tested on both gray
scale and RGB iris images of CASIA database [9].
In Fig. 4, a comparison of Peak Signal to Noise
Ratio (PSNR) of ten interpolated RGB iris images
using nearest neighbour, bilinear, bicubic, iNEDIand proposed method is plotted using MatLab. The
PSNR values of different methods have been
summarized in tabular format in table 1. The
average PSNR for each method is calculated which
are 30.353 for new method, 30.138 for iNEDI
method, 19.463 for Bilinear method, 22.506 for
Bicubic method and 17.669 for Nearest neighbourmethod. The results clearly show the improved
PSNR performance of proposed method as compare
to conventional methods.
In Fig. 5, an original iris image of size
320x280 is taken and small portion of this image of
size 45x30 is cropped out of it as shown. This
cropped image is then zoomed up using windows
photo gallery and using new proposed method to
size of 350x200. In Fig. 6, images of size 50×50
have been interpolated to size 400 x 400 usingvarious techniques such as zooming, nearest
neighbour interpolation, bilinear interpolation,
bicubic interpolation, iNEDI method and proposed
interpolation method. The result shows the visual
quality improvements of new proposed method as
compared to others mentioned. The result shows
visually that employing the enhancement can helpin identification of iris image pattern matching
techniques.
Figure 4: Matlab plot of PSNR of different interpolation methods of RGB iris images.
Figure 5: Cropping Application of New Proposed Method
Table 1: The table summarizes the PSNR in Decibels (dB) of Iris images enhanced using different interpolation
methods.
Images 1 2 3 4 5 6 7 8 9 10
Hybrid 30.37 29.9 30.83 30 29.93 30 30.3 30.4 31 30.8
iNEDI 30.27 29.84 30.44 29.8 29.43 29.9 30.1 30.13 30.9 30.57
Bilinear 19.53 18.96 19.87 19.35 19 19.33 19.41 19.58 19.95 19.65
Bicubic 22.56 22.11 22.93 22.11 22.45 22.41 22.55 22.59 22.5 22.85
Nearest 17.58 17.36 17.94 17.46 17.37 17.46 17.53 17.6 18.24 18.15
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(a) Original image
(b) Simple zooming of image
(c) Nearest neighbor interpolation
(d) Bilinear interpolation
(e) Bicubic interpolation
(f) iNEDI interpolation method
(g) Hybrid interpolation method
Figure 6: Interpolation methods employed to enhance 50×50 size iris image taken from CASIA database to
200×200 size for visual quality comparison. a) Original image, (b) zoomed image, (c) Nearest neighborinterpolation, (d) Bilinear interpolation, (e) Bicubic interpolation, (f) iNEDI interpolation, (g) Hybrid
interpolation
4 CONCLUSION
The proposed method is a hybrid technique for
enhancing iris images in order to aid the
recognition process of iris recognition system. The
algorithm employs new covariance based
interpolation for edges and iterative curvature based
interpolation for smooth areas. A threshold isselected by performing an iterative experiment.
This threshold differentiates between an edge and
smooth areas. The results showed improved PSNR
performance and visually enhanced iris images with
the propose method as compare to conventional
methods.
5 REFERENCES
[1] Nanavati S, T. M: Biometrics Identity
Verification in A Networked World, New
York: John Wiley & Sons, In (2002).
[2] Rafael C. Gonzalez, R. E: InterpolationTechniques, Digital Image Processing,
Pearson Prentice Hall (2007).
[3] HS Hou, H.C: Cubic Splines for Image
Interpolation and Digital Filtering, IEEE
Transactions Acoustics, Speech, Signal
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Processing. , pp. 508-517 (1978).
[4] Huang J. T.T. M.L. W.Y: Learning Based
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[8] B.S Morse, D.S: Isophate-Based Interpolation,IEEE International Conference on Image
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[9] Database: Center for Biometrics and Security
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[10] Aftab, Hassan Mansoor, Atif Bin Asim,
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