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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorMuazzam, Shakib
dc.contributor.authorAli, Tahsin Mohammad
dc.contributor.authorShuvo, Md. Mehedi Hasan
dc.contributor.authorKaisar, Nabid
dc.date.accessioned2017-07-09T06:24:15Z
dc.date.available2017-07-09T06:24:15Z
dc.date.copyright2017
dc.date.issued2017-04-18
dc.identifier.otherID 13101074
dc.identifier.otherID 12241002
dc.identifier.otherID 12101071
dc.identifier.otherID 13101011
dc.identifier.urihttp://hdl.handle.net/10361/8266
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.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 19-20).
dc.description.abstractThis 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.statementofresponsibilityShakib Muazzam
dc.description.statementofresponsibilityTahsin Mohammad Ali
dc.description.statementofresponsibilityMd. Mehedi Hasan Shuvo
dc.description.statementofresponsibilityNabid Kaisar
dc.format.extent20 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.subjectFeature extraction techniqueen_US
dc.subjectPerson identificationen_US
dc.titleA new feature extraction technique for person identificationen_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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