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dc.contributor.advisorUddin, Jia
dc.contributor.authorNafees, Masnoon
dc.contributor.authorFuad, Md. Shamsuzzaman
dc.date.accessioned2017-06-14T05:58:29Z
dc.date.available2017-06-14T05:58:29Z
dc.date.copyright2017
dc.date.issued4/18/2017
dc.identifier.otherID 13101173
dc.identifier.otherID 12101015
dc.identifier.urihttp://hdl.handle.net/10361/8243
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 26).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractThe high similarity between identical twin is known to be a great challenge for Face Recognition Technology. As Face Recognition Technology handles identification and the verification of identity claim of a person, it is really important to have a method which can overcome identical twin problem. In this research, we try to demonstrate a model which can predict and compare identical twin. In this method, we used histogram, RGB colors to find the best criteria for matching for initial stage. Later, we used GLCM technology which measure the texture analysis of the images where some parameters are being used for calculation. After the analysis, we delivered a conclusion based on our results we found.en_US
dc.description.statementofresponsibilityMasnoon Nafees
dc.description.statementofresponsibilityMd. Shamsuzzaman Fuad
dc.format.extent26 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectFacial recognitionen_US
dc.subjectGLCMen_US
dc.titleA twin prediction method using facial recognition featureen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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