Iris Recognition in Less Constrained Environment

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013)

196

Iris Recognition in Less Constrained Environment

Pradeep Manikrao Patil

Vidya Pratishthan College Of Engineering, Baramati, India

Abstract— Biometric based on the physical and behavioural characteristics are widely adopted and are used to recognize person uniquely in a natural and intuitive way. Biometric traits are unique to the individual. Among biometric traits such as face, signature, thumb etc, Iris biometric is the most reliable authentication method. Most of the commercial iris biometric systems operate in a constrained environment. Iris images captured in the constrained environment have sufficient information to discriminate individual from another. Such iris recognition system shows good recognition rate but Performance of the system degrades in noisy environment. This paper presents the study of iris recognition in less constrained environment. Also the challenges are discussed.

Keywords— Biometrics, iris recognition, Non ideal iris recognition, phase based method, pattern recognition, Texture analysis.

I. INTRODUCTION

Biometric is an automated method to recognize a person based on his physiological and behavioural characteristics such ear, face, fingerprints, hand shape, iris, palm print, retina, signature, voice etc. Among these techniques, iris recognition is characterized as accurate, reliable and stable biometric trait for personal recognition. The human iris is the colour annular part of the eye between the black pupil and white sclera(Fig. 1)[1]. The iris texture is divided into pupillary zone and ciliary zone. Iris textures are unique. It is an internal protected organ and remains stable over individual‘s life time. It is difficult to modify iris texture These characteristics makes iris particularly useful for personal identification [2][3][4].

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The accuracy of iris recognition system is higher than other biometric systems like signature, fingerprint and face recognition. This accuracy depends on the quality of the captured iris image data. The environment in which iris image data is captured can be categorized as a constrained Environment and less constraint environment. In constrained environment the distance between the camera and subject iris is limited (less than one meter), have proper illumination, iris is in the same gaze with the camera.

Figure I: Iris Image [1]

These images contain less noise. In less constrained environment iris images data affected by different type of noise such as iris occlusion due to eyelid and eyelash, off-angle, specular reflection, lightning variations, motion blur etc. The performance of iris recognition system depends on the quality of the data. Performance degrades with the presence of the noise. In applications like surveillance application where user‘s cooperation is not expected, lightning conditions may vary. In such less constrained environment iris image contains noise and due to presence of noise the performance of iris recognition system degrades.

Typical applications of iris recognition in less constrained environment includes surveillance applications, missing child detection, access to high security area/ building, forensic application etc. In such applications user‘s cooperation is not expected. If we are able to address these problems then it is possible to deploy iris recognition system in large application domains.

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International Journal of Emerging Technology and Advanced Engineering

197 II. IRIS RECOGNITION

Typical iris recognition system consists of following modules.

1. Iris Image acquisition module

2. Segmentation Module

3. Feature Extraction module 4. Comparison module

1. Iris Image Acquisition Module:

This module deals with acquisition of iris images using camera. In less constrained environment capturing good quality iris images is a challenging task. The Iris on the move project is the major example of acquisition of images in less constrained environment. In this system iris images of sufficient quality are captured while subject is moving at a normal walking pace [5].

Iris images are captured using NIR (near-infrared) illumination or VW (Visible Wavelength)[12]. Majority of the iris recognition systems are based on the near-infrared imaging. Iris is less sensitive to NIR imaging, but eliminates the information in pigment melanin. Images captured under VW preserves fine details and contain more useful information. Iris is sensitive to VW. Images captured in VW introduce specular reflections.

Sensar inc. and Sarnoff research centre developed non-intrusive image acquisition system using narrow field of view camera and cross correlation based algorithm [6].

Figure: II : Noisy Iris images[24]

Park and Kim [7] proposed fast acquisition of in-focus images. Daugman‘s system captures images from a distance of 15 - 45cm. Iris images captured in the less constrained environment contain noise in the form of defocus blur, off angle images, occlusion and lightning variation. Fig.II shows some noisy iris images[24].

2. Segmentation Module:

Iris segmentation is the crucial component in any iris recognition system. Segmentation inaccuracies can degrade recognition performance.

Processing non ideal iris images is a challenging task because captured images contains different types of noise. For non-ideal iris images different segmentation methods are proposed. Most iris segmentation methods proposed in the literature assumes pupillary and iris boundaries are either circular or elliptical in shape. Also they use edge detection followed by circular Hough transform.

Daugman used integro-differential operator for segmentation and is defined as [2] :

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r

y

x

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Where I(x,y) is image domain, Gσ( r ) is a Gaussian function with scale σ. , r is radius and (x0 , y0) are centre

coordinates. The image is scanned for circle.

Daugman‘s approach is good for images with high contrast. In less constrained environment this approach will not be effective if images do not have sufficient intensity between iris and sclera. For detecting inner and outer iris boundary he proposed active counter based method[3][4].

H. Proenca and Alexandre uses edge map followed by circular hough transform approach. They construct edge map using fuzzy clustering approach[11]. The constructed edge map is less dependent on any image characteristics.

In [12] H. presented neural network based method for iris segmentation. They used feed-forward neural network. The experimental results were carried out on degraded iris images. These images were acquired using visible wavelength (VW).

Samir Shah and Arun Ross [1] described a method for segmentation of non-ideal iris images using geodesic active counter. They use local and global properties of image. For pupil boundary detection they smoothed iris image using 2- D median filter. Using median filtered binary image exterior boundaries of the object are traced. For limbic boundary detection geodesic active contours model used. The matching performance of segmentation algorithm was evaluated on WVU and CASIA V3-Interval iris image database.

Jinyu Zauo and Natalia Schmid[13] proposed method for segmentation of non-ideal iris images. Specular reflections degrade the performance of segmentation algorithm. To remove specular reflection, Inpainting of specular reflections is performed. 2-D wiener filter is used to eliminate the noise effect. Circular Hough transform is used for pupil localization and segmentation.

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International Journal of Emerging Technology and Advanced Engineering

198 In the first stage algorithm estimates the inner and outer iris boundary using elliptical mode, in the second stage they apply modified Mumford-Shah functional to compute iris boundaries.

3. Iris Feature Extraction Module

Iris feature extraction methods are categorized as phase based method, zero-crossing representation method and texture analysis based method.

Daugman[2][3][4], uses 2-D Gabor wavelet to extract iris texture phase information. He decomposed signals using a quadrature pair of Gabour filters. The output of the Gabour filter is demodulated by quantizing the phase information into four level and are represented using two bits of data. Total 2048 phase bits are computed for each iris and equal numbers of masking bits are computed in order to mask out the region obscured by noise such as occlusion due to eyelids and eyelash, specular reflections. The basis for iris recognition is the failure of test of statistical independence involving degree of freedom. The test of statistical independence was implemented by Exclusive-OR (XOR) operator. The dissimilarity between two iris patterns, whose phase code bit vector and mask bit vectors are denoted by codeA, codeB and maskA, maskB respectively, is computed using Hamming distance (HD) given in equation (2).

maskB

maskA

maskB

maskA

codeB

codeA

HD

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Hamming distance is fractional measure of dissimilarity. HD equals to 0 represents a perfect match between two irises. EER using Daugman method is 0.08%

In encoding Gabor filters over represents the low frequency components and under represents the high frequency components. Even-symmetric Gabor filters contains a DC component if the bandwidth is larger than one octave. For any bandwidth a zero DC components can be obtained using Log-Gabor filter[15]. The frequency response of a Log-Gabor filter, with central frequency f0

and filter bandwidth σ is given in equation (3)

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In [15], Libor Masek , A 2-D normalized iris pattern is converted into number of 1-D signals. The features are encoded by convoluting 1-D signals with 1-D Log-Gabor wavelet.

Mayank Vasta [14], extracted iris texture features using 1-D log polar Gabor transform. Extracted features are rotation, shift, scale, contrast and illumination invariant.

Wilds et.al [28], uses four level Laplacian pyramid to extract iris features. They use Laplacian of Gaussian filter to encode iris image. The filter is :

2 2_/₂

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Where σ is the standard deviation of Gaussian is and a point is at a radial distance ρ from the centre of the filter.

Matching is based on the normalized correlation between images.

Ma et.al[17][18] used bank of special filters to extract iris texture features. They used bootstrap learning and Fisher discriminant to improve recognition rate.

In [19], Amol D. Rahulkar and Raghunath S. Holambe presented a shift, scale, and rotation-in-variant method for iris feature extraction. A new class of a triplet 2-D orthogonal wavelet basis was designed. Iris features oriented in horizontal, vertical and diagonal direction are computed by using wavelet basis. The inner half iris region is divided into six sub images and only four regions are selected for further processing. Triplet half-band filter bank (THFB) is applied separately on each subimages. The feature vector for each sub image is derived by estimating the channel energies of the THFB. They introduced a k-out-of-n:A(Accept) post classifier to improve the speed and accuracy of the iris recognition system. This approach (THFB+ k-out-of-n:A) is able to handle segmentation error, occlusion due to eyelid/eyelashes, specular reflection, head-tilt, shadow of eyelids. Feature extraction using THFB, and k-out-of-n:A postclassifier significantly reduces FRR also provides low computational complexity which makes it feasible for online applications.

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International Journal of Emerging Technology and Advanced Engineering

199 The extracted information is normalized to have same number of data points and a zero crossing representation is generated. The normalized information and zero crossing representation are periodic and independent from the starting point in iris virtual circle. This information is stored as a signature. The dissimilarity between the irises of the same eye is small. The amount of the computation is significantly reduced since the number of zero crossing is less than the total number of data points. The problem with this function is that it requires compared representations to have small number of zero crossing at each resolution level. EER using Bole and Boashash methodology is 8.13%.

D. de Martin-Roche, C. Avila and R. Sanchez-Reillo[21] developed iris feature extraction method based on fine to coarse approximations at different resolutions levels based on the discrete dyadic wavelet transform zero- crossing representation. An iris signature of 256 bits has been obtained. Authors claim good results

III. IRIS DATABASE

Most widely used iris image database is CASIA[9]. CASIA-IrisV4 Lamp, contains heterogeneous images. Iris images from Multimedia University(MMU)[25] database presents few noise factors. Images from University of Bath are quite similar to the ones contained in MMU.

TABLEII NOISY IRIS DATABASE

Database Version-Subset

No of images

Features

CASIA V4-Lamp 16,212

Nonlinear deformation due to variation of visible illumination

UBIRIUS

V1 1877

Images with several noise factors

V2 11102

Images captured in less constrained condition, with more realistics noise factors

MMU

MMU1 450

Subjects are from Asia,Middle east,Africa, & Eurpoe

MMU2 995 Noisy images

IIT Delhi V 1.0 1120

Images with noise factors

They have similar characteristics and few noise factors, almost exclusively related with small eyelid or eyelash obstructions. UBIRIS [24] database was built during September 2004. UBIRIS V2 contains images captured in non-constrained conditions using visible wavelength with corresponding more realistic noise factors. IIT Delhi database contains 1120 iris images [26]. Some noisy iris databases are listed in table II.

IV. CONCLUSION

Iris pattern is unique to an individual and is stable throughout the life time of a human being. Because of its unique characteristics, iris recognition provides one of the most secure method of authentication. Performance of iris recognition system in less constrained environment depends on the image quality and segmentation accuracies. This demands new methods to capture iris images at a distance with enough quality. In this paper, an attempt has been made to present an insight of different iris recognition methods. This study provides a platform for the future developments in less constrained iris biometric system.

REFERENCES

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[2] John Daugman, "How Iris Recognition Works," IEEE Transactions On Circuits And Systems For Video Technology, vol. 14, no. 1, pp.21-30, Jan. 2004.

[3] John Daugman, "High Confidence Visual Recognition By Test Of Statistical Independence," IEEE Transactions On Pattern Analysis And Machineintelligence, vol. 15, pp. 1148-1161, November 1993. [4] John Daugman, "New methods in iris recognition", IEEE Transction

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[5] James R. Matey, Oleg Naroditsky, Keith Hanna, Ray Kolczynski, Dominick J. LoIacono, Shakuntala Mangru, Michael Tinker,Thomas M. Zappia, and Wenyi Y. Zhao, "Iris on The Move: Acquisition of images for iris recognition in less constrained environments," in Proc. of the IEEE, vol. 94, no. 11, pp. 1936-1947, Nov. 2006. [6] K. Hanna, R. Mandelbaum, D. Mishra, V. Paragano, L. Wixson, "A

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[10] Huo Proenca, ―Quality assement of degraded iris images acquired in the visible wavelength,‖ IEEE Transction On Information Forensics And Security, VOL. 6, no. 1, pp. 82–95, Mar. 2010. [11] Huo Proença and Luıs A. Alexandre, "Iris segmentation

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[13] Jinyu Zuo, Schmid, N.A, "On a Methodology for robust Segmentation of Nonideal Iris Images", IEEE Trans. on Systems ,Man and Machine and Cybernetics Part B: Cybernetics , vol. 40, no. 3, pp. 703–718, June 2010.

[14] Mayank Vatsa, Richa Singhieee, And Afzel Noore, "Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing," IEEE Trans. On Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 38, no. 4, pp.1021-1035, Aug. 2008

[15] Libor Masek,‖ Recognition of Human Iris Patterns for biometric identification‖, School of Computer Science and SoftEngineering, The University of Western Australia, 2003

[16] T. Camus and R. Wildes,"Reliable and fast eye finding in close-up images," In The Proceedings Of International Conference On Pattern Recognition, pp. 389–394, 2002.

[17] 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, pp. 1519-1533,Dec. 2003.

[18] Li Ma,"Efficient iris recognition by characterizing key local variations," IEEE Trans. On Image Processing, vol. 13, no. 6, pp. 739-749, June 2004.

[19] Amol D. Rahulkar and Raghunath S. Holambe,"Half-iris feature extraction and recognition using a new class of biorthogonal triplet half-band filter bank and flexible k-out-of-n:A Postclassifier," IEEE Trans. On Information Forensics And Security, vol. 7, no. 1, pp.230-240, Feb. 2012.

[20] 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, pp.1185-1188.

[21] D. de martin-Roche, C. Sanchez-Avil & R. Sanchez-Reillo,‖ iris Recognition For Biometric Identification using Dyadic Wavelet Transform Zero-Crossing‖, IEEE 2001 pp.272-277.

[22] W. W. Boles, "A wavelet transform based technique for the recognition of the human iris," International Symposium on Signal Processing and its Applications, ISSPA, Gold Coast, Australia, pp. 601-604, Aug. 1996.

[23] Hugo Proença and Luis A. Alexandre, "Toward noncooperative iris recognition: a classification approach using multiple signatures," IEEE Trans. On Pattern Analysis And Machine Intelligence, vol. 29, no. 4, pp. 607-612, Apr. 2007.

[24] UBIRIS Iris Database http://iris.di.ubi.pt/ubiris2.html

[25] Multimedia University, MMU Iris Image Database, http://pesona.mmu.edu.my/~ccteo/

[26] IIT Delhi Iris database, http://www.iitd.ac.in [27] http://www.inf.upol.cz/iris/