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dc.contributor.advisorBin Ashraf, Faisal
dc.contributor.authorZaman, Sara Milham
dc.contributor.authorHasan, Md. Abir
dc.contributor.authorSadat, Md. Rafid
dc.contributor.authorHaque, Md. Abrar
dc.date.accessioned2023-08-13T06:43:26Z
dc.date.available2023-08-13T06:43:26Z
dc.date.copyright2023
dc.date.issued2023-01
dc.identifier.otherID: 19101141
dc.identifier.otherID: 18201019
dc.identifier.otherID: 22341053
dc.identifier.otherID: 19101648
dc.identifier.urihttp://hdl.handle.net/10361/19384
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 45-49).
dc.description.abstractDetailed Fingerprint investigation has been a dominant law enforcement tool which is utilized to distinguish suspects, settle crimes and violations for over 100 years. Moreover, gender classification from fingerprints is a vital step in forensic anthropol ogy in order to identify a criminal’s gender and reduce the list of suspects. A novel approach of machine learning (ML) which is One Shot Learning has been intro duced in this report for identification of persons which will implement the Siamese learning approach for training fingerprint samples by using the triplet loss. One Shot Learning has shown to be efficient because it reliably performs with only one labeled training example and one or a few training sets. Moreover, by using Transfer Learn ing with EfficientNetV2S an accuracy of 99.80%, 99.73%, 97.09%, 99.66%, 98.61% for identification of person, gender, hand, finger and detection of forge fingerprints has been achieved on the Sokoto Coventry Fingerprint Dataset.en_US
dc.description.statementofresponsibilitySara Milham Zaman
dc.description.statementofresponsibilityMd. Abir Hasan
dc.description.statementofresponsibilityMd. Rafid Sadat
dc.description.statementofresponsibilityMd. Abrar Haque
dc.format.extent49 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectFingerprinten_US
dc.subjectOne shot learningen_US
dc.subjectSiamese learningen_US
dc.subjectMachine learningen_US
dc.subjectTransfer learningen_US
dc.subjectTriplet lossen_US
dc.subjectEfficientNetV2Sen_US
dc.subjectSOCOFing Dataseten_US
dc.subject.lcshBiometric identification.
dc.titleComprehensive fingerprint recognition utilizing one shot learning with Siamese Networken_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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