dc.contributor.advisor | Uddin, Jia | |
dc.contributor.author | Nafees, Masnoon | |
dc.contributor.author | Fuad, Md. Shamsuzzaman | |
dc.date.accessioned | 2017-06-14T05:58:29Z | |
dc.date.available | 2017-06-14T05:58:29Z | |
dc.date.copyright | 2017 | |
dc.date.issued | 4/18/2017 | |
dc.identifier.other | ID 13101173 | |
dc.identifier.other | ID 12101015 | |
dc.identifier.uri | http://hdl.handle.net/10361/8243 | |
dc.description | Cataloged from PDF version of thesis report. | |
dc.description | Includes bibliographical references (page 26). | |
dc.description | This 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.abstract | The 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.statementofresponsibility | Masnoon Nafees | |
dc.description.statementofresponsibility | Md. Shamsuzzaman Fuad | |
dc.format.extent | 26 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | BRAC 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.subject | Facial recognition | en_US |
dc.subject | GLCM | en_US |
dc.title | A twin prediction method using facial recognition feature | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering | |