Exploratory simulation of an Intelligent Iris Verifier Distributed System

This paper discusses some topics related to the latest trends in the field of evolutionary approaches to iris recognition. It presents the results of an exploratory experimental simulation whose goal was to analyze the possibility of establishing an Interchange Protocol for Digital Identities evolved in different geographic locations interconnected through and into an Intelligent Iris Verifier Distributed System (IIVDS) based on multi-enrollment. Finding a logically consistent model for the Interchange Protocol is the key factor in designing the future large-scale iris biometric networks. Therefore, the logical model of such a protocol is also investigated here. All tests are made on Bath Iris Database and prove that outstanding power of discrimination between the intra- and the inter-class comparisons can be achieved by an IIVDS, even when practicing 52.759.182 inter-class and 10.991.943 intra-class comparisons. Still, the test results confirm that inconsistent enrollment can change the logic of recognition from a fuzzified 2-valent consistent logic of biometric certitudes to a fuzzified 3-valent inconsistent possibilistic logic of biometric beliefs justified through experimentally determined probabilities, or to a fuzzified 8-valent logic which is almost consistent as a biometric theory - this quality being counterbalanced by an absolutely reasonable loss in the user comfort level.

[1]  Dexin Zhang,et al.  DCT-Based Iris Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Valentina Emilia Balas,et al.  Learning Iris Biometric Digital Identities for Secure Authentication: A Neural-Evolutionary Perspective Pioneering Intelligent Iris Identification , 2012, Recent Advances in Intelligent Engineering Systems.

[3]  Donald M. Monro,et al.  Robust Iris Feature Extraction and Matching , 2007, 2007 15th International Conference on Digital Signal Processing.

[4]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[7]  Valentina Emilia Balas,et al.  From Cognitive Binary Logic to Cognitive Intelligent Agents , 2010, 2010 IEEE 14th International Conference on Intelligent Engineering Systems.

[8]  Valentina Emilia Balas,et al.  Comparing Haar-Hilbert and Log-Gabor based iris encoders on Bath Iris Image Database , 2010, 4th International Workshop on Soft Computing Applications.

[9]  George W. Quinn,et al.  IREX I: Performance of Iris Recognition Algorithms on Standard Images | NIST , 2009 .

[10]  Neil Yager,et al.  The Biometric Menagerie , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Tieniu Tan,et al.  Iris recognition: recent progress and remaining challenges , 2004, SPIE Defense + Commercial Sensing.

[12]  John Daugman,et al.  Biometric decision landscapes , 2000 .

[13]  K.W. Bowyer,et al.  The Best Bits in an Iris Code , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.