Review of Iris Recognition System Iris Recognition System Iris ...

IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 633- 637

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Review of Iris Recognition System Shivani1, Er. Puja Kaushik2, Er. Yuvraj Sharma3 1

Student of Electronic & Communication Engineering M. M. Engineering College, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India [email protected] 2

Assistant Professor Department of Electronic & Communication Engineering M. M. Engineering College, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India [email protected]

3

Assistant Professor Department of Electronic & Communication Engineering M. M. Engineering College, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India [email protected]

Abstract Iris recognition is an important biometric method for human identification with high accuracy. It is the most reliable and accurate biometric identification system available today. This paper gives an overview of the research on iris recognition system. The most popular biometric types are: signature, face, iris, finger prints, hand and voice. Among all these biometric techniques, iris recognition is one of the accurate due to its high reliability for personal identification. So this paper deals with the review of all the existing techniques for iris recognition system.

Keywords: Iris Recognition, Personal Identification.

1. INTRODUCTION 1.1 The human iris The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. A fronton view of the iris is shown in Figure1. The iris is perforated close to its centre by a circular aperture known as the pupil. The function of the iris is to control the amount of light entering through the pupil, and this is done by the sphincter and the dilator muscles, which adjust the size of the pupil. The average diameter of the iris is 12 mm, and the pupil size can vary from 10% to 80% of the iris diameter [2]. Iris Upper Eyelid

pupil

Sclera Lower Eyelid Fig. 1 The human eye

Shivani, IJRIT

633


The unique patterns in the human iris are formed by 10 month of birth, and remain unchanged throughout one’s lifetime. Iris is a thin, circular structure in the eye. It controls the diameter and size of the pupils and thus the amount of light reaching the retina. Eye color is the color of the iris, which in humans may look green, blue, brown, hazel (a combination of light brown, green and gold) grey, violet or even pink. When the amount of light entering the eye, muscles attached to the iris expand the center or the iris.

1.2 Pupil The pupil is the black opening in the centre of the eye. Light enters through the pupil and goes through the lens, which focuses the image on the retina. The size of the pupil is controlled by muscles. When more light is required, the pupil expands as per the need. In bright light, the pupil becomes smaller. The function of the iris is to control the amount of light entering through the pupil. The larger the pupil, the more light can enter into the pupil [2].

1.3 Advantages of iris recognition system Uniqueness: The iris pattern is unique as copmpare to the other pattern because of texture details in iris image, such as freckles, coronas, stripes and furrows. Even twins do not have the same iris pattern. They have totally different iris details. It is the most reliable and informative biometric pattern. Stability: The iris texture is to be formed during gestation and the main structures of iris are shaped after 10 months of birth. It has also been shown that the iris is essentially stable across ones’s lifetime. Scalability: Image of the iris region can be normalized into rectangle regions so that binary feature codes of fixed length can be extracted. Therefore iris recognition is used for large-scale personal identification applications. Security: The iris recognition is more secure simply because of unique pattern. Human iris has many special and physiological characteristics. This technology is very secure as compare to the other. There are many technologies used for iris recognition available today, latest technologies are as follows: 1. Morlet wavelet transform coefficient. 2. K-d tree method. 3. Biorthogonal wavelet. 4. Reverse biorthogonal wavelet.

1) Morlet wavelet transform coefficient Zhonghua Lin, Bibo Lu et.al dicussed the morlet wavelet transform technique [10], [18], [19].The fundamental idea behind wavelets is to analyze according to scale. They discussed iris recognition method using morlet wavelet transform coefficients. Four main stages were used. Segmentation or localization of the image, normalization, feature extraction, lastly matching of image with the stored data.

Localization

Normalization

Feature Extraction

Matching

Fig. 2 Iris Recognition System

localization: The localization is used to localize the iris boundary as well as pupil boundary. The boundries is to be detected with the help of edge direction operator. The inner and outer boundries of the iris are calculated. Normalization: The next step is normalizattion. Polar-coordinate is a two dimensional coordinate system in which each point on a plane is determined by a distance from a fixed point and an angle from a fixed direction. Main aim of normalization is to convert the cartisian-coordinate into polar-coordinate.

Shivani, IJRIT

634


Feature extraction: Feature extaraction is implimented with wavelet function. Wavelet function is always equal to the window function therefore, it solves the problem between the time resolution and frequency resolution [10]. Then in this recognition system morlet wavelet is used for feature extraction. Iris match: The last step of iris recognition is matching the image with the stored database.

2) K-d tree method Steve Zhou and Junping Sun discussed the K-d tree technique [1].This method is used to organize the iris feature vectors for known IDs. The feature vectors for unknown individual traversed the tree to search for the match and discover its identity.

Iris Image Pupil Detection Iris Detection Localization Eyelid Detection Eyelash Detection Normalization Feature Extraction K-D Tree matching

Iris Image Dataset

Fig. 3 Iris Recognition Process In the first step segmentation and normalization on an image performed.. Iris feature extraction: Feature extraction is implemented using the (1-D) log gabor wavelet. A 1-D Gabor filter decomposed the normalized iris signal. Multiple bytes of normalized iris image was broken into multiple vectors [1]. Each 1-D vector consisted of a number of bytes and corresponded to one row of the iris image then the 1-D log-gabor wavelet algorithm was applied. Matching: The K-d tree method is to be used for iris code searching and recognition. This method is used for organizing the iris code of known IDs. Lastly matches the image with the stored database.

3) Biorthogonal wavelet Aditya Abhyankar and Lawerence Hornak discussed biorthogonal technique [17], [20]. The biorthogonal wavelet used for lifting technique to encode the iris information. The author describes the bi-orthogonal wavelet based iris recognition. The algorithm can be divided into four steps:

Segmentation

Normalization

Encoding

Matching

Fig. 4 Iris Recognition system Shivani, IJRIT

635


Step for normalization and segmentation are same as in the previous method. Template formation or encoding: Both segmented and normalized iris information is transformed into the wavelet domain using the biorthogonal wavelet. The filters are used for lifting steps. Filter is used for all the iris image and formulated bit pattern is called as iris template. Matching: Match is used for phase information converted to ones and zereos are encoded from the normalized iris patterns. Lastly templates are matched and similarity score is calculated.

4) Reverse biorthogonal wavelet Zaheera Zainal Abidin and Mazani Manaf discussed reverse biorthogonal wavelet technique [3]. It is kind of wavelet which is correlated to a wavelet transform. The use of reverse biorthogonal wavelet, gives freedom in designing in any system compare to orthogonal wavelets [3]. This paper on compression in wavelet decomposition for security in biometric data. The objectives of this research are two folds: a) To investigate whether compressed human eye image differ with the original eye. b) To obtain the compression ratio values using proposed methods. The only change in this technique is the feature extraction with the existing ones. The reverse biorthogonal used for feature extraction.

2. CONCLUSION In this paper, different techniques of iris recognition system are dicussed. Iris recognition is unique as compare to the other existing technique. Iris pattern may be used for reliable visual recognition. Many methods for iris pattern on the basis of CASIA or UBIRIS database are studied in this paper. K-d tree method is very long process method for iris recognition system. The results of morlet wavelet transform method are better and also this method is short process as compare to the k-d tree method. From the research review, it has been observed that iris recognition rate(IRR) for K-d tree and morlet wavelet transform is 99.64% [1], [10]. But the iris recognition rate(IRR) for reverse biorthogonal wavelet is 99.65% [3]. Finally, it has been observed that reverse biorthogonal wavelet transform gives better results as compared to K-d tree, morlet wavelet and biorthogonal methods.

3. REFERENCES [1] Steve Zhou and Junping Sun “A Novel Approach for Code Match in Iris Recognition”,IEEE International Conference on Computer And Information Science, June 2013. [2] Sukhwinder Singh, Ajay Jatav, “A Closure Looks To Iris Recognition System”, International Journal Of Scientific & Engineering Research, Vol. 3, March 2013. [3] Zaheer Zainal Abidin, Mazani Manaf, “Experimental Approach On Thresholding Using Reverse Biorthogonal wavelet decomposition For Eye Image” IEEE,2013. [4] Adams Wai-Kin Kong, “Modeling IrisCode and Its Variants as Convex Polyhedral Cones and Its Security Implications” IEEE Transactions On Image Processing, Vol. 22, March 2013. [5] Bimi Jain, Dr.M.K.Gupta, Prof.JyotiBharti, “ Efficient Iris Recognition Algorithm Using Method Of Moments” International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, September 2012. [6] Javier Galballya, Arun Rossb, Marta Gomez-Barreroa, Julian Fierreza, Javier Ortega-Garciaa “A New Vulnerability Of Iris Recognition Systems”, 2012 [7] B. Thiyaneswaran , S. Padma, “Iris Recognition using Left and Right Iris Feature of the Human Eye for Bio-Metric Security System”, International Journal of Computer Applications, Vol.50 , July 2012. [8] Yu Li, Zhou Xue Fast, “Iris Boundary Location Based on Window Mapping Method” Seventh International Conference on Computational Intelligence and Security, 2011 [9] Gite H.R., Mahender C.N. “Iris Code Generation And Recognition” International Journal Of Machine Intelligence Vol. 3, 2011 [10] Zhonghua Lin Bibo, “Iris Recognition Method Based on the Imaginary Coefficients of Morlet Wavelet Transform” , “Seventh International Conference on Fuzzy Systems and Knowledge Discovery “2010. [11] Zhonghua Lin, “ A Novel Iris Recognition Method Based on theNatural-open Eyes” IEEE ,2010.

Shivani, IJRIT

636


[12] Fei Tan and Zhengming Li, “Iris Localization Algorithm Based on Gray Distribution Features”, IEEE , 2010. [13] Lin Zhonghua, Lu Bibo, “Adaptive Iris Recognition Method Based on the Marr Wavelet Transform Coefficients” School of Computer Science and Technology Henan Polytechnic University,2009. [14] Jing-Hui Li, “New Algorithm of Iris Localization”,World Congress on Computer Science and Information Engineering IEEE,2009. [15] John Daugman, “ New Methods In Iris Recognition”, IEEE Transactions On Systems, Vol. 37, October 2007. [16] J.Lu and M.Xie, “ A new method for iris localization”, IEEE, 2007. [17] Traian Dogaru and Lawrence Carin, “Multiresolution Time-Domain Using CDF Biorthogonal Wavelets”, IEEE Transactions On Microwave Theory And Techniques, Vol. 49, May 2001 [18] Samuel S. Osofsky, “ Calculation Of Transient Sinusoidal Signal Amplitudes Using The Morlet Wavelet” , IEEE Transactions On Signal Processing, Vol. 47, December 1999. [19] Shyh-Jier Huang, Cheng-Tao Hsieh and Ching-Lien Huang, “Application of Morlet Wavelets to Supervise Power System Disturbances”,IEEE Transactions on Power Delivery, Vol. 14, No. 1, January 1999. [20] Dong Wei, Jun Tian, Raymond O. Wells, Jr., and C. Sidney Burrus, “A New Class of Biorthogonal Wavelet Systems for Image Transform Coding”, IEEE Transactions On Image Processing, Vol. 7, No. 7, July 1998

Shivani, IJRIT

637