Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

Divya Ann Roy*, Urmila S. Soni**
* PG Scholar, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.
** Associate Professor, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.
Periodicity:January - March'2016

Abstract

Iris recognition is a special type of biometric system, which is used to identify a person by analyzing the patterns in the iris. It is used to recognize the human identity through the textural characteristics of one's iris muscular patterns. Although eye colour is dependent on heredity, iris is independent even in the twins. Out of various biometrics such as finger and hand geometry, face, ear and voice recognition, iris recognition has been proved to be one of the most accurate and reliable biometric modalities because of its high recognition. Iris recognition involves 5 major steps. Firstly, image acquisition is done in which the image is captured by a high resolution camera, then the iris and the pupillary boundary are extracted out from the whole eye image, which is called segmentation. After segmentation, the circular dimension is converted to a fixed rectangular dimension which is called normalization. From this normalised image, the feature is extracted from Gabor filter, DFT, FFT, etc. At last, the iris code is matched using Hamming distance and Euclidean method. This project focuses on iris segmentation. Iris segmentation is the most important part in the iris recognition process because the areas that are wrongly considered as the iris regions would corrupt the biometric templates resulting in a very poor recognition [16]-[21]. The main objective of iris segmentation is to separate the iris region from the pupil and sclera boundaries. There are various methods for segmenting the iris from an eye image to give a best segmented result. In this project, iris segmentation is done using Daugman's integro differential method and Circular Hough Transform to find out the pupil and the iris boundaries. Iris images are taken from the CASIA V4 database, and the iris segmentation is done using Matlab software where iris and pupilary boundaries are segmented out. The experimental result shows that 84% accuracy is obtained by segmenting the iris by Circular Hough Transform and 76% accuracy is obtained by segmenting the iris through Daughman's method. It is concluded that, the Circular Hough Transform method of iris recognition is more accurate than the Daughman's method.

Keywords

Keywords: Daughman, Segmentation, Recognition, Biometric, Circular Hough Transform.

How to Cite this Article?

Roy, D.A., and Soni, U.S. (2016). Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method. i-manager’s Journal on Image Processing, 3(1), 29-36.

References

[1]. Ahmad Ayatollahi and Seyedzade Seyed Mohammad (2010). “A Novel Iris Recognition System Based th on Active Contour”. 17 Iranian Conference of Biomedical Engineering (ICBME2010).
[2]. Anawar S, Ayop Z and M. Manaf, (2012). “Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System”. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 4.
[3]. Adegoke, B. O, Omidiora, E. O, Falohun, S. A. and Ojo, J.A. (2013). “Iris Segmentation: A survey”. International Journal of Modern Engineering Research, Vol.3, No. 4.
[4]. Basu Somak and Dubey Maheedhar, (2013). “Daughman’s Algorithm Method For Iris Recognition: A Biometric Approach”. International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 6.
[5]. Bendale Amit and Gupta Phalguni, (2012). “Iris Segmentation using an Improved Hough Transform”. Springer 2012.
[6]. Chawla Sunil and Oberoi Ashish, (2011). “A Robust Segmentation Method for Iris, Recognition”. International Journal of Advanced Research in Computer Science, Vol. 2, No. 5.
[7]. Chen Yen, Chang Han, Andrim Jean and Rishe N. (2009). “A highly accurate computationally efficient approach for unconstrained iris segmentation”. Elsevier.
[8]. Cherabit Noureddin, Chelali Fatma Zohra and Djeradi Amar, (2012). “Circular Hough Transform for Iris localization”. Science and Technology.
[9]. Daugman J, (2007). “New Methods in Iris Recognition”. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 37, No. 5.
[10]. Dubey R. B and Madan Abhimanyu, (2014). Iris Localization using Daugman's Intero-Differential Operator, Vol. 93, No 3.
[11]. Djoumessi Maeva, (2012). “Iris Segmentation using Daughman's Intro-Differential Operator”. Conference Paper, University of North Carolina, Chapel Hill.
[12]. Dobeš M, Martineka M, Skoupila D, Dobešováb Z, and Pospíšilc J, (2005). “Human eye localization using the modified Hough transform”. Elsevier.
[13]. Gupta Rajeev and Kumar Ashok, (2013). “An Effective Segmentation Technique for Noisy Iris Images”. International Journal of Application or Innovation in Engineering & Management, Vol. 2, No. 12.
[14]. Ito Y, Ohyama W, Wakabayashi T, and Kimura T, (2012). “Detection of Eyes by Circular Hough Transform and st Histogram of Gradient”. 21 International Conference on Pattern Recognition (ICPR 2012).
[15]. James Sahaya Mary, (2015). “A Review of Daugman's Algorithm in Iris Segmentation”. International Journal of Innovative Science, Engineering & Technology, Vol. 2, No. 8.
[16]. Kayaoglu M, and Uludag U, (2015). “Biometric Matching and Fusion System for Fingerprints from Non- Distal Phalanges”.
[17]. Kadry S, (2007). “A design and implementation of a wireless iris recognition attendance management system”. Information Technology and Control, Vol.36, No. 3.
[18]. Kaur Navjot, (2014). “An Iris Localization Method for Non-ideal Eye Images”. Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 4.
[19]. Krishna M, and Reddy S, (2014). “Automated Iris Localization using Active Contour Model for IRIS Recognition”. International Journal of Ethics in Engineering & Management Education, Vol. 1, No. 2.
[20]. More Manisha, Narale Vishakha and Tonge Vanita, (2015). “A Survey on Iris Recognition Techniques”. International Journal of Novel Research in Computer Science and Software Engineering, Vol. 2, No. 1.
[21]. Nguyen Thanh, Kien and Fookes, Clinton B. and Sridharan Sridha, (2010). “Fusing shrinking and expanding th active contour models for robust IRIS segmentation”. 10 International Conference on Information Science, Signal Processing and their Applications, 10-13 May 2010, Renaissance Hotel, Kuala Lumpur.
[22]. Nithyanandam S, Gayathri K. S, and Priyadarshini P. L. K, (2011). “A New Iris Normalization Process For Recognition System With Cryptographic Techniques”. IJCSI International Journal of Computer Science Issues, Vol. 8, No. 4, No 1.
[23]. Nkole Ifeanyi Ugbaga, Sulong Ghazali Bin, and Saparudin (n.d). “An Enhanced Iris Segmentation Algorithm Using Circle Hough Transform”. International Conference, Kudai, Johor, Malaysia.
[24]. Rizon M, Yazid H, Saad P, and Saad A. R, (2005). “Object Detection using Circular Hough Transform”. American Journal of Applied Sciences, Vol. 2, No. 12, pp. 1606-1609.
[25]. Ross Arun and Shah Samir, (2009). “Iris Segmentation using Geodesic Active Contours”. IEEE Transactions, Vol. 4, No. 4.
[26]. Soltany Milad, Zadeh Saeid Toosi and Pourreza Hamid-Reza, (2012). “Daugman's Algorithm Enhancement for Iris Localization”. Advanced Materials Research, pp. 403-408
[27]. Suen Ching Y, Bhattacharya Prabir, and Roy Kaushik, (2010). “Unideal Iris Segmentation using Region-Based Active Contour Mode”. Springer-Verlag, Berlin, Heidelberg 2010.
[28]. Sundaresan M, and S. Jayalakshmi, (2014). “A Study of Iris Segmentation Methods using Fuzzy C Means and KMeans Clustering Algorithm”. International Journal of Computer Applications, Vol. 85, No 11.
[29]. T. K Sruthi, and K. M Jini, (2013). “A Literature Review on Iris Segmentation Techniques for Iris Recognition Systems”. Vol. 11, No. 1.
[30]. Walia Mrigana and Jain Shaily, (2015). “Iris Recognition System Using Circular Hough Transform”. International Journal of Advance Research in Computer Science and Management Studies, Vol. 3, No. 7.
[31]. Zhang Zhengben and Wang Chongke, (2015). “Research on Iris Localization Algorithm based on the Active Contour Model”. International Journal of Security and Its Applications, Vol. 9, No. 3.
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