Study Of Iris Recognition Schemes By Ritika Jain

Study Of Iris Recognition Schemes By: Ritika Jain ritika. jain@mavs. uta. edu Under guidance of DR K R RAO UNIVERSITY OF TEXAS AT ARLINGTON SPRING 2012

PROPOSAL This project is focussed upon studying and implementing the various iris recognition schemes available and an analysis of the different algorithms using Chinese academy of sciences institute of automation (CASIA) [14] database.

AN INTRODUCTION[19] Biometric technology is widely used for personnel identity identification. A biometric system provides automatic identification of an individual. Typical biometric technologies include fingerprint identification, face recognition, iris recognition etc. Iris recognition is regarded as the most reliable and accurate biometric identification system available.

A GOOD BIOMETRIC[19] A good biometric is characterized by use of a feature that is: • highly unique – so that the chance of any two people having the same characteristic will be minimal • stable – so that the feature does not change over time • can be easily captured – in order to provide convenience to the user • prevent misrepresentation of the feature.

IRIS RECOGNITION[19] Iris recognition is amongst the most robust and accurate biometric technique available in the market today with existing large scale applications supporting databases in excess of millions of people. The iris is a protected organ whose random texture is stable throughout the life and hence can be used as an identity document offering a very high degree of identity assurance.

ADVANTAGES OF USING IRIS AS A RECOGNITION SCHEME [19] § Iris is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor. § The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil. This makes the iris shape far more predictable than, for instance, that of the face.

• There is no need for the person being identified to touch any equipment that has recently been touched by a stranger, thereby eliminating an objection that has been raised in some cultures against fingerprint scanners, where a finger has to touch a surface, or retinal scanning, where the eye must be brought very close to an eyepiece (like looking into a microscope) • An iris scan is similar to taking a photograph and can be performed from about 10 cm to a few meters away. Figure 1 shows the location of iris.

IRIS Figure 1: The human eye with the location of iris. [19]

DISADVANTAGES OF USING IRIS FOR IDENTIFICATION [19] • As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates. • Many commercial Iris scanners can be easily fooled by a high quality image of an iris or face in place of the real thing. • The accuracy of scanners can be affected by changes in lighting • As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure to enroll rates.

OVERVIEW In the project the various algorithms are discussed analyzed like as proposed by Daugman [2], [5], [6]; Recognition of human iris patterns for biometric identification as proposed by Masek [10], [11]; Phase based iris identification by Miyawaza [15], discrete cosine transform (DCT) [20] based Iris recognition by Monro et al [16] and other like techniques available. The various functions, filters and the processes involved will be studied and the results are compared on the basis of previous studies of that approach. CASIA iris image database [14] will be used for images of iris to be analyzed for the different codes.

SUMMARY OF WORKING OF BIOMETRIC SYSTEMS [3] Biometric systems work by: • first capturing a sample of the feature • sample is then transformed using some sort of mathematical function into a biometric template • the biometric template will provide a normalized, efficient and highly discriminating representation of the feature (which can then be objectively compared with other templates in order to determine identity).

• most biometric systems allow two modes of operation : an enrolment mode and an identification mode.

LIBOR MASEK'S PRINCIPLE [3] The iris recognition system consists of : • automatic segmentation system that is based on the Hough transform [3] and is able to localize the circular iris and pupil region • The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. • Finally, the phase data from 1 D Log-Gabor filters [3] was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template.

The Hamming distance [3] was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed.

The system is composed of a number of sub-systems [3], which correspond to each stage of iris recognition. These stages are as follows: • segmentation – locating the iris region in an eye image • normalization – creating a dimensionally consistent representation of the iris region • feature encoding – creating a template containing only the most discriminating features of the iris The input to the system is an eye image, and the output is be an iris template, which will provide a mathematical representation of the iris region.

By understanding the techniques available and doing a comparative study of their advantages, disadvantages and efficiency, an analysis will be made about the features involved, the advantages and the shortcomings and the results will be compared with the previous studies conducted using the different methods. The project will be extended to modify the code to include a more vast database with increased genuine detection.

SCOPE AND FUTURE EXTENSION The project can be extended to include a vast database with increased genuine detection which involves forming more templates and improvising the current code. The hardware of the equipment can be worked upon and improvised which is used to capture the image to improve the performance.

REFERENCES • [1] J. Daugman, "High confidence visual recognition of persons by a test of statistical independence", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148 -1160, November, 1993. • [2] J. Daugman, " How iris recognition works", IEEE Transactions on circuits and systems for video technology, Vol. 14, No. 1, pp. 21 -30, January, 2004. • [3] L. Masek, "Recognition of human iris patterns for biometric identification", M. S. thesis, University of Western Australia, 2003. • [4] R. Wildes, " Iris recognition: an emerging biometric technology", Proceedings of the IEEE, Vol. 85, No. 9, pp. 1348 -1363, September, 1997. • [5] J. Daugman, Biometric personal identification system based on iris analysis. United States Patent, Patent Number: 5, 291, 560, 1994.

• [6] S. Sanderson and J. Erbetta, " Authentication for secure environments based on iris scanning technology", IEE Colloquium on Visual Biometrics, pp. 8/1 -8/7, March, 2000. • [7] R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey and S. Mc. Bride, " A system for automated iris recognition", Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, pp. 121 -128, December, 1994. • [8] W. Boles and B. Boashash, " A human identification technique using images of the iris and wavelet transform", IEEE Transactions on Signal Processing, Vol. 46, No. 4, pp. 185 -1188, April, 1998. • [9] A. Gongazaga and R. M. da Costa, " Extraction and selection of dynamic features of human iris", IEEE Computer Graphics and Image Processing, Vol. XXII, pp. 202 -208, October, 2009.

• [10] P. Kovesi "MATLAB functions for computer vision and image analysis", available at: http: //www. cs. uwa. edu. au/~pk/Research/Matlab. Fns/index. ht ml. • [11] L. Masek and P. Kovesi, “MATLAB source code for a biometric identification system based on iris patterns’’, The school of computer science and software engineering, The university of western Australia, 2003. • [12] D. M. Monro, S. Rakshit and Z. Dexin, "DCT based iris recognition”, IEEE Transactions on pattern analysis and machine intelligence, Vol. 29, Issue 4, pp. 586 -595, April, 2007. • [13] Different sample source codes available at: Advancedsourcode. com: http: //www. advancedsourcecode. com/iris. asp

• [14] Chinese academy of sciences - institute of automation, database of greyscale eye images http: //www. cbsr. ia. ac. cn/Iris. Database. htm • [15] K. Miyazawa, K. Ito, K. Aoki, T. Kobayashi and K. Nakajima, " An efficient iris recognition algorithm using phase based image matching ", IEEE International conference on image processing, pp. 325 -328, September, 1995. • [16] W. Kong and D. Zhang, " Accurate iris segmentation based on novel reflection and eyelash detection model", Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, pp. 263 -266, May, 2001.

• [17] N. Ritter, "Location of the pupil-iris border in slit-lamp images of the cornea", Proceedings of the International Conference on Image Analysis and Processing, pp. 740 -745, September, 1999. • [18] Y. Zhu, T. Tan and Y. Wang, ” Biometric personal identification based on iris patterns” , Proceedings of the 15 th International Conference on Pattern Recognition, Spain, Vol. 2, pp. 801 -804, February, 2000. • [19] Online free encyclopedia, Wikipedia: http: //www. wikipedia. org/. • [20] K. R. Rao and P. Yip, ”Discrete cosine transform”, Boca Raton, FL: Academic press, 1990.

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