{"created":"2020-09-01T13:03:07.480135+00:00","id":3491,"links":{},"metadata":{"_buckets":{"deposit":"53fcaa40-8cbe-44fa-8f75-f3b2699ffdd4"},"_deposit":{"id":"3491","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3491"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3491","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Fingerprint Type Classification Using Learning Vector Quantization","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"This paper proposes a fingerprint typesclassification algorithm using Learning VectorQuantization (LVQ) with FingerCode features. Thisalgorithm assigns each fingerprint image to one ofthe five subclasses, according to the Henry system:Arch(A), Tented Arch(T), Left Loop(L), RightLoop(R), and Whorl Loop(W). The search for aspecific fingerprint can therefore be performed onlyon specific subclasses containing a small portion of alarge database, which will save enormouscomputational time. We use the feature vectors fromFingerCode generation process to train with theLVQ classifiers. In our feature extraction process,the oriented components are extracted from afingerprint image using a bank of Gabor filters, anda feature vector is computed for each orientedcomponent. The feature vectors from the input imageare classified using LVQ classifier. This algorithmhas been tested the fingerprint database. For the 100fingerprint images, the classification accuracy is 93%, with 7 % error rate for 5-classes."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-24"}],"displaytype":"preview","filename":"psc2010paper (32).pdf","filesize":[{"value":"575 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3491/files/psc2010paper (32).pdf"},"version_id":"0d03fdb5-2842-4eb9-96dd-98f718553ff0"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Fifth  Local Conference on Parallel and Soft Computing","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Phyo, Aye Su"},{"subitem_authors_fullname":"Sandar, Khin"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2010-12-16"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1236"},"item_title":"Fingerprint Type Classification Using Learning Vector Quantization","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-24","publish_status":"0","recid":"3491","relation_version_is_last":true,"title":["Fingerprint Type Classification Using Learning Vector Quantization"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T05:53:52.938491+00:00"}