Learning Iris Biometric Digital Identities for Secure Authentication: A Neural-Evolutionary Perspective Pioneering Intelligent Iris Identification
This chapter discusses the latest trends in the field of evolutionary approaches to iris recognition, approaches which are compatible with the task of multi-enrollment in a biometric authentication system based on iris recognition, and which are also able to ensure strong discrimination between the enrolled users. A new authentication system based on supervised learning of iris biometric identities is proposed here. It is the first neural-evolutionary approach to iris authentication that proves an outstanding power of discrimination between the intra- and inter-class comparisons performed for the test database (Bath Iris Image Database). It is shown here that when using digital identities evolved by a logical and intelligent artificial agent (Intelligent Iris Verifier/Identifier) the separation between inter- and intra-class scores is so good that it ensures absolute safety for a very large percent of accepts (97%, for example), i.e. recognition is no longer a statistical event, or in other words, the statistical aspect of iris recognition becomes residual while the logical binary aspect prevails. In this way, iris recognition theory and practice advance from inconsistent verification to consistent verification/identification.
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- 3.Daugman, J.: Biometric Decision Landscapes, Technical Report No. TR482, University of Cambridge (2000), http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-482.pdf
- 4.Daugman, J.: How Iris Recognition Works. IEEE Trans. on Circuits and Systems for Video Technology 14(1) (January 2004)Google Scholar
- 6.Dong, W., Tan, T., Sun, Z.: Iris Matching Based on Personalized Weight Map. Accepted for Publication in IEEE-TPAMI (2010) (to appear) Google Scholar
- 7.Grother, P., Tabassi, E., Quinn, G., Salamon, W.: Interagency report 7629: IREX I - Performance of iris recognition algorithms on standard images. N.I.S.T (October 2009)Google Scholar
- 9.Iris Challenge Evaluation, N.I.S.T., http://iris.nist.gov/ice/ (cited February 20, 2011)
- 11.Monro, D.M., Rakshit, S.: Rotation Independent Iris Matching by Motion Estimation. In: Proc. IEEE Int. Conf. on Image Processing (September 2007)Google Scholar
- 13.Popescu-Bodorin, N., Balas, V.E.: AI Challenges in Iris Recognition. Processing Tools for Bath Iris Image Database. In: Proc. 11th Int. Conf. on Automation and Information, pp. 116–121. WSEAS Press (June 2010)Google Scholar
- 15.Popescu-Bodorin, N., Balas, V.E.: Comparing Haar-Hilbert and Log-Gabor based iris encoders on Bath Iris Image Database. In: Proc. 4th Int. Conf. on Soft Computing Applications, pp. 191–196. IEEE Press, Los Alamitos (2010)Google Scholar
- 16.Popescu-Bodorin, N., State, L.: Cognitive Binary Logic - The Natural Unified Formal Theory of Propositional Binary Logic. In: Recent Advances in Computational Intelligence, pp. 135–142. WSEAS Press (April 2010)Google Scholar
- 18.Rakshit, S., Monro, D.M.: Pupil Shape Description Using Fourier Series. In: Workshop on Signal Processing Applications for Public Security and Forensics (April 2007)Google Scholar
- 19.Rakshit, S., Monro, D.M.: Robust Iris Feature Extraction and Matching. In: Proc. IEEE Int. Conf. on Digital Signal Processing (July 2007)Google Scholar
- 20.Tan, T., Ma, L.: Iris Recognition: Recent Progress and Remaining Challenges. In: Proc. of SPIE, vol. 5404, pp. 183–194 (April 2004)Google Scholar
- 21.Wildes, R.: Iris Recognition - an emerging biometric technology. Proc. of the IEEE 85(9),1348–1363 (1997)Google Scholar