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A new feature extraction technique for person identification

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dc.contributor.advisor Uddin, Dr. Jia
dc.contributor.author Muazzam, Shakib
dc.contributor.author Ali, Tahsin Mohammad
dc.contributor.author Shuvo, Md. Mehedi Hasan
dc.contributor.author Kaisar, Nabid
dc.date.accessioned 2017-07-09T06:24:15Z
dc.date.available 2017-07-09T06:24:15Z
dc.date.copyright 2017
dc.date.issued 2017-04-18
dc.identifier.other ID 13101074
dc.identifier.other ID 12241002
dc.identifier.other ID 12101071
dc.identifier.other ID 13101011
dc.identifier.uri http://hdl.handle.net/10361/8266
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 Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 19-20).
dc.description.abstract This paper proposes a one of a kind model of human distinguishing proof by which we can supplant the persevering work of individuals if there should arise an occurrence of security. Our technique is for the most part in view of human structure identification and face detection with Microsoft Kinect V1.0. With a specific end goal to apply our approach, 6 points of 20 in body structure and 26 points out of 121 in face detection is taken as input. These points are X, Y, Z coordinates outputted by Kinect skeletal and facial following output by Kinect skeletal and facial tracking. 16 unique distances are then calculated by the Euclidean distance formula using the coordinates. These are the selective components extracted from each user and afterward put in a database. At long last, by coordinating calculation our framework distinguishes known or obscure users progressively. Final output is then given as a result with aptitude and noteworthy precision. en_US
dc.description.statementofresponsibility Shakib Muazzam
dc.description.statementofresponsibility Tahsin Mohammad Ali
dc.description.statementofresponsibility Md. Mehedi Hasan Shuvo
dc.description.statementofresponsibility Nabid Kaisar
dc.format.extent 20 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 Feature extraction technique en_US
dc.subject Person identification en_US
dc.title A new feature extraction technique for person identification 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


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