Facial Emotion Recognition using Convolutional Neural Network with Multiclass Classification and Bayesian Optimization for Hyper Parameter Tuning. 2022 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave

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.

sted, utgiver, år, opplag, sider

2022. , s. 43

Emneord [en]

Computing Methodologies, Machine Learning, Machine Learning Approaches, Convolutional Neural Network, Facial Emotion Recognition.

HSV kategori

Identifikatorer

URN: urn:nbn:se:bth-23359OAI: oai:DiVA.org:bth-23359DiVA, id: diva2:1677831

Fag / kurs

DV1478 Bachelor Thesis in Computer Science

Utdanningsprogram

DVGDT Bachelor Qualification Plan in Computer Science 60.0 hp

Presentation

2022-05-23, Karlskrona, 08:15 (engelsk)

Veileder

Examiner

Tilgjengelig fra: 2022-07-01 Laget: 2022-06-28 Sist oppdatert: 2025-09-30bibliografisk kontrollert

Open Access i DiVA

Av organisasjonen

Om emnet

Søk utenfor DiVA

GoogleGoogle ScholarTotalt: 4583 nedlastingerTotalt: 1118 treff