Commenced in January 2007
Paper Count: 30172
Improved Feature Processing for Iris Biometric Authentication System
Abstract:Iris-based biometric authentication is gaining importance in recent times. Iris biometric processing however, is a complex process and computationally very expensive. In the overall processing of iris biometric in an iris-based biometric authentication system, feature processing is an important task. In feature processing, we extract iris features, which are ultimately used in matching. Since there is a large number of iris features and computational time increases as the number of features increases, it is therefore a challenge to develop an iris processing system with as few as possible number of features and at the same time without compromising the correctness. In this paper, we address this issue and present an approach to feature extraction and feature matching process. We apply Daubechies D4 wavelet with 4 levels to extract features from iris images. These features are encoded with 2 bits by quantizing into 4 quantization levels. With our proposed approach it is possible to represent an iris template with only 304 bits, whereas existing approaches require as many as 1024 bits. In addition, we assign different weights to different iris region to compare two iris templates which significantly increases the accuracy. Further, we match the iris template based on a weighted similarity measure. Experimental results on several iris databases substantiate the efficacy of our approach.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083243Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1773
 J. G. Daugman. High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148-1161, November 1993.
 John Daugman. Iris Recognition. American Scientist, 89:326-333, July- August 2001.
 J. Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21- 30, 2004.
 W. W. Boles and B. Boashash. A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transaction on Signal Processing, 46(4):1185-1188, 1998.
 M. Vasta, R. Singh, and A.Noore. Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Fearures. International Journal of Signal Processing, 2(2):66-72, 2005.
 Jafar M. H. Ali and Aboul Ella Hassanien. An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory. AMO - Advanced Modeling and Optimization journal, 5(2):93-104, 2003.
 Shinyoung Lim, Kwanyong Lee, Okhwan Byeon, and Taiyun Kim. Efficient Iris Recognition through Improvement of Feature Vector and Classifier. ETRI Journal, 23(2):61-70, June 2001.
 R. P. Wildes. Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE, 85(9):1348-1363, September 1997.
 L. Ma, T. Tan, D. Zhang, and Y. Wang. Local Intensity Variation Analysis for Iris Recognition. Pattern Recognition, 37(6):1287-1298, 2004.
 L. Ma, Y. Wang, and T. Tan. Iris recognition based on multichannel gabor filtering. In Proc. of the 5th Asian Conference on Computer Vision, volume I, pages 279-283, 2002.
 Li Ma, Tieniu Tan, Yunhong Wang, and Dexin Zhang. Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing, 13(6):739-750, June 2004.
 L. Ma, T. Tan, Y. Wang, and D. Zhang. Personal Identification Based on Iris Texture Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(12):1519-1533, December 2003.
 Jaemin Kim, Seongwon Cho, Jinsu Choi, and II Robert J. Marks. Iris recognition using wavelet features. J. VLSI Signal Process. Syst., 38(2):147-156, 2004.
 Y. Zhu, T. Tan, and Y. Wang. Biometric Personal Identification Based on Iris Patterns. In Proc. of the 15th International Conference on Pattern Recognition, volume II, pages 805-808, 2000.
 Tai Sing Lee. Image representation using 2d gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10):959- 971, 1996.
 B. S. Manjunath and W. Y. Ma. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):837-842, 1996.
 J. Shen, W. Shen, and D. F. Shen. On geometric and orthogonal moments. International Journal of Pattern Recognition and Artificial Intelligence, 14(7):875-894, 2000.
 L. Ma, Y. Wang, and T. Tan. Iris Recognition Using Circular Symmetric Filters. In Proc. of the 16th International Conference on Pattern Recognition, volume II, pages 414-417, 2002.
 J. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, 36(2):279-292, February 2003.
 S. Dey and D. Samanta. Efficient and accurate approach to iris segmentation. Technical report, School of Information Technology, July 2007.
 S. Mallat and W. Hwang. Singularity detection and processing with wavelets. IEEE Transaction on Information Theory, 38(2):617-643, 1992.
 A. Jensen and A. la Cour-Harbo. Ripples in Mathematics: The Discrete Wavelet Transform. Springer, 2001.
 University of Bath iris image database, 2007. http://www.bath.ac.uk/elec-eng/research/sipg/irisw.
 Hugo Proenc┬©a and Lu'─▒s A. Alexandre. Ubiris: A noisy iris image database. In Fabio Roli and Sergio Vitulano, editors, ICIAP, volume 3617 of Lecture Notes in Computer Science, pages 970-977. Springer, 2005.
 Multimedia University iris image database. http://pesona.mmu.edu.my/╦£ccteo/.
 CASIA iris image database. http://www.sinobiometrics.com.