Comparative analysis between Inception-v3 and other learning systems using facial expressions detection

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.issued8/18/2016
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.
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.
dc.identifier.otherID 16141024
dc.identifier.otherID 12201020
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/c314bbb5-da1d-4d07-ad73-fb39be5b2fb9
dc.identifier.urihttp://hdl.handle.net/10361/6397
dc.language.isoen
dc.publisherBRAC University
dc.sourceBRAC University Institutional Repository
dc.subjectInception-V3
dc.subjectFacial expressions detection
dc.titleComparative analysis between Inception-v3 and other learning systems using facial expressions detection
dc.typeThesis

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