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

[email protected]

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.



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].



Figure 1: Illustration Interpolation scheme of proposed method

Figure 2: New Covariance based Interpolation Scheme

Figure 3: Iterative Curvature based Interpolation Scheme



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



(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



Processing. , pp. 508-517 (1978).

[4] Huang J. T.T. M.L. W.Y: Learning Based

Resolution Enhancement of Iris Images,

British Machine Vision Conference., pp. 153-

162 (2003).

[5] X. Li, M. T. Orchard: New edge-directed

interpolation. s.l. : IEEE Trans. on ImageProcessing, Vols. 10, pp. 1521-1527 (October,

2001).

[6] Nicola Asuni, Andrea Giachetti: Accuracy

improvements and artifact removal in edge

based image interpolation. s.l. : Proc. 3rd Int.

Conf. Computer Vision Theory and

Applications, pp. 8 (2008).[7] Andrea Giachetti, Nicola Asuni: Fast

Artifacts- Free Image Interpolation. Leeds,

UK : Proceedings of the British Machine

Vision Conference (BMVC), pp. 10

(September, 2008).

[8] B.S Morse, D.S: Isophate-Based Interpolation,IEEE International Conference on Image

Processing , pp 227-231 (1998).

[9] Database: Center for Biometrics and Security

Research. Retrieved 2009, from Institute of

Automation chinese Academy of Sciences:

http://www.cbsr.ia.ac.cn/english/IrisDatabase.

asp

[10] Aftab, Hassan Mansoor, Atif Bin Asim,

Muhammad ‘A New single image

interpolation technique for super resolution’,

INMIC 2008, 23-24 Dec. Karachi , Pakistan

2008, page 592-596

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