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Date of Award


Document Type

Thesis - Pacific Access Restricted

Degree Name

Master of Science (M.S.)


Engineering Science

First Advisor

Anahita Zarci

First Committee Member

Louise Stark

Second Committee Member

Ken Hughes

Third Committee Member

Jinzhu Gao

Fourth Committee Member

Jennifer Ross


Image categorization is often performed manually, which can be a time consuming and a very difficult process, especially for human iris images. Previous researchers have been working on predicting ethnicity from texture features of iris images using other methods. This thesis is one of the the first to present a solution of iris image categorization using artificial neural networks, specifically for human iris images with discernible and complicated textures. The work will allow users to quickly and automatically categorize human iris images by using supervised and unsupervised learning algorithms. Contributions of this solution include a fast and accurate way to apply iris matching and solve the time consuming problems. The solution aims to find efficient and appropriate artificial neural network algorithms that can categorize iris images based on texture features. Detailed algorithms, specific techniques, performance analysis, limitations and future work will be also provided in this thesis.



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