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Date of Award
2014
Document Type
Thesis - Pacific Access Restricted
Degree Name
Master of Science in Engineering (M.S.Eng.)
Department
Engineering
First Advisor
Anahita Zarei
First Committee Member
John Mayberry
Second Committee Member
Louise Stark
Abstract
This research attempts to use a method that can reduce the search time of a system trying to match a user's iris image against those in a very large database. One method to reduce search time is to predict an individual's ethnicity and then only search iris templates belonging to that particular ethnicity in the database. By limiting the search to a small subsection of the database, as opposed to an exhaustive search, this method will cut significant run time from the search process. We propose the use of an adaptive neuro-fuzzy inference system to implement this idea. In addition to the adaptive neuro-fuzzy inference system, we also used the subtractive clustering technique to create membership functions that helped us compare how each iris' texture features were clustered. By combining subtractive clustering with the adaptive neuro-fuzzy inference system, we were able to map each user to a singular value that identified with a specific ethnicity based on their iris characteristic features. This method achieves a classification rate of 80% while maintaining an acceptable search time.
Pages
55
ISBN
9781303996887
Recommended Citation
Tang, Gary L.. (2014). Fuzzy inference systems for iris biometrics to reduce search time in large databases. University of the Pacific, Thesis - Pacific Access Restricted. https://scholarlycommons.pacific.edu/uop_etds/230
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