{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/3241"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/3241","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"An Extended Local Binary Pattern for Gender Classification","abstract":"The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.","abstract_html":"The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.","abstract_has_math":false,"creators":["Roayaei Ardakany, Abbas"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nicolescu, Mircea"],"committee_chairs":[],"committee_members":["Nicolescu, Monica","Shen, Shantao"],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-27T21:45:45Z","subjects":["Gender Recognition","Genetic Algorithms","Image Processing","Local Binary Patterns"],"languages":[],"rights":["In Copyright(All Rights Reserved)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/3241","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nicolescu, Mircea"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Nicolescu, Monica","Shen, Shantao"]},{"key":"dc:creator","label":"Author","values":["Roayaei Ardakany, Abbas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-05-01T12:35:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-01T12:35:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2013"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gender Recognition","Genetic Algorithms","Image Processing","Local Binary Patterns"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright(All Rights Reserved)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/3241"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["An Extended Local Binary Pattern for Gender Classification"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nicolescu, Mircea"],"dc:contributor.committeemember":["Nicolescu, Monica","Shen, Shantao"],"dc:creator":["Roayaei Ardakany, Abbas"],"dc:date.accessioned":["2018-05-01T12:35:01Z"],"dc:date.available":["2018-05-01T12:35:01Z"],"dc:date.issued":["2013"],"dc:description.abstract":["The face is one of the most important biometric features of humans, conveying race, identity, age, gender and facial expression information, among which gender plays a significant role in social interactions. An automatic gender recognition system has many applications in computer-human interaction, psychology, security, demographic and business issues. In this work, we designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, we proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. We have evaluated our approach on the standard FERET and CAS-PEAL databases and our experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. Our results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy."],"dc:format":["PDF"],"dc:identifier.uri":["http://hdl.handle.net/11714/3241"],"dc:rights":["In Copyright(All Rights Reserved)"],"dc:subject":["Gender Recognition","Genetic Algorithms","Image Processing","Local Binary Patterns"],"dc:title":["An Extended Local Binary Pattern for Gender Classification"],"dc:type":["Thesis"],"thesis:degree_level":["Master's Degree"]},"updated_at":"2026-07-27T21:45:45Z"}