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dc.contributor.advisorChakrabarty, Dr. Amitabha
dc.contributor.advisorMostakim, Moin
dc.contributor.authorNivrito, AKM
dc.contributor.authorWahed, Md. Rayed Bin
dc.date.accessioned2016-09-08T09:14:30Z
dc.date.available2016-09-08T09:14:30Z
dc.date.copyright2016
dc.date.issued8/18/2016
dc.identifier.otherID 16141024
dc.identifier.otherID 12201020
dc.identifier.urihttp://hdl.handle.net/10361/6397
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 33-35).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.description.abstractIn the last five years or so, Machine Learning has taken the world by storm. From predictive web browsing, to E-mail classification, to autonomous cars; machine learning is at the heart of every intelligent applications that’s in service today. Image Classification and Facial Expression Recognition is another field that has benefited immensely from the emergence of this technology. In particular, an branch of Machine Learning called Deep Learning, has shown tremendous results in this regard even outperforming more conventional methods such as Image Processing. Inspired by neurons in the human brain, Artificial Neural Networks, allow us to map complex functions by stacking layers upon layers of these networks. Our goal in this paper, is to analyze Inception v-3, the best performing high resolution image classifier based on Convolutional Neural Network out there today, with other methods including one of our own to see how it performs on low resolution images detect Facial Expressions.en_US
dc.description.statementofresponsibilityAKM Nivrito
dc.description.statementofresponsibilityMoin Mostakim
dc.format.extent35 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.subjectInception-V3en_US
dc.subjectFacial expressions detectionen_US
dc.titleComparative analysis between Inception-v3 and other learning systems using facial expressions detectionen_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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