bth.se
EnglishSvenskaNorsk
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Facial Emotion Recognition using Convolutional Neural Network with Multiclass Classification and Bayesian Optimization for Hyper Parameter Tuning.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2022 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The thesis aims to develop a deep learning model for facial emotion recognition using Convolutional Neural Network algorithm and Multiclass Classification along with Hyper-parameter tuning using Bayesian Optimization to improve the performance of the model. The developed model recognizes seven basic emotions in images of human beings such as fear, happy, surprise, sad, neutral, disgust and angry using FER-2013 dataset.
Place, publisher, year, edition, pages
2022. , p. 43
Keywords [en]
Computing Methodologies, Machine Learning, Machine Learning Approaches, Convolutional Neural Network, Facial Emotion Recognition.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-23359OAI: oai:DiVA.org:bth-23359DiVA, id: diva2:1677831
Subject / course
DV1478 Bachelor Thesis in Computer Science
Educational program
DVGDT Bachelor Qualification Plan in Computer Science 60.0 hp
Presentation
2022-05-23, Karlskrona, 08:15 (English)
Supervisors
Examiners
Available from: 2022-07-01 Created: 2022-06-28 Last updated: 2022-07-01Bibliographically approved

Open Access in DiVA

Facial Emotion Recognition using Convolutional Neural Network with Multiclass Classification and Bayesian Optimization for Hyper Parameter Tuning(690 kB)3907 downloads
File information
File name FULLTEXT02.pdfFile size 690 kBChecksum SHA-512
204fb0040acada95f2f570df10bfa36820b36d5e5a20278d2b4b1d94ae788fc7309a976bdeec455e3fbfb2bb45abc548bb8d46875325a2dafbc99af5e4966ad7
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 3908 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 921 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.45.0
|
WCAG
|
BTH Library
|
Publish/Register
|
How to publish/register
|
Diva portal
|
SwePub
|
Uppsök