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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Mou, Duxing"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis - Pacific Access Restricted","degree_discipline":"Engineering Science","degree_department":null,"school":null,"contributors":["Anahita Zarci"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T05:36:52Z","subjects":["Iris (Eye);Ethnicity;Neural networks (Computer science)","Engineering"],"languages":[],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholarlycommons.pacific.edu/uop_etds/856","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anahita Zarci"]},{"key":"dc:creator","label":"Author","values":["Mou, Duxing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Pacific Access Restricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Iris (Eye);Ethnicity;Neural networks (Computer science)","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarlycommons.pacific.edu/uop_etds/856"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:source","label":"Dc Source","values":["166"]},{"key":"dc:title","label":"Title","values":["Human iris categorization using artificial neural networks"]}]}],"canonical_facts":{"dc:contributor":["Anahita Zarci"],"dc:creator":["Mou, Duxing"],"dc:date.available":["2013-01-01T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarlycommons.pacific.edu/uop_etds/856"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:source":["166"],"dc:subject":["Iris (Eye);Ethnicity;Neural networks (Computer science)","Engineering"],"dc:title":["Human iris categorization using artificial neural networks"],"thesis:degree_discipline":["Engineering Science"],"thesis:degree_level":["Thesis - Pacific Access Restricted"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T05:36:52Z"}