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

Cataloged from PDF version of thesis report.

Xehetasun bibliografikoak
Egile Nagusiak:	Nivrito, AKM, Wahed, Md. Rayed Bin
Beste egile batzuk:	Chakrabarty, Dr. Amitabha
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	BRAC University 2016
Gaiak:	Inception-V3 Facial expressions detection
Sarrera elektronikoa:	http://hdl.handle.net/10361/6397

id	10361-6397
record_format	dspace
spelling	10361-63972022-01-26T10:20:04Z Comparative analysis between Inception-v3 and other learning systems using facial expressions detection Nivrito, AKM Wahed, Md. Rayed Bin Chakrabarty, Dr. Amitabha Mostakim, Moin Department of Computer Science and Engineering, BRAC University Inception-V3 Facial expressions detection Cataloged from PDF version of thesis report. Includes bibliographical references (page 33-35). This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. In 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. AKM Nivrito Moin Mostakim B. Computer Science and Engineering 2016-09-08T09:14:30Z 2016-09-08T09:14:30Z 2016 8/18/2016 Thesis ID 16141024 ID 12201020 http://hdl.handle.net/10361/6397 en 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. 35 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Inception-V3 Facial expressions detection
spellingShingle	Inception-V3 Facial expressions detection Nivrito, AKM Wahed, Md. Rayed Bin Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
description	Cataloged from PDF version of thesis report.
author2	Chakrabarty, Dr. Amitabha
author_facet	Chakrabarty, Dr. Amitabha Nivrito, AKM Wahed, Md. Rayed Bin
format	Thesis
author	Nivrito, AKM Wahed, Md. Rayed Bin
author_sort	Nivrito, AKM
title	Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
title_short	Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
title_full	Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
title_fullStr	Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
title_full_unstemmed	Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
title_sort	comparative analysis between inception-v3 and other learning systems using facial expressions detection
publisher	BRAC University
publishDate	2016
url	http://hdl.handle.net/10361/6397
work_keys_str_mv	AT nivritoakm comparativeanalysisbetweeninceptionv3andotherlearningsystemsusingfacialexpressionsdetection AT wahedmdrayedbin comparativeanalysisbetweeninceptionv3andotherlearningsystemsusingfacialexpressionsdetection
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Comparative analysis between Inception-v3 and other learning systems using facial expressions detection

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