Facial Emotion Recognition using Convolutional Neural Network with Multiclass Classification and Bayesian Optimization for Hyper Parameter Tuning.

Description

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.

Links & Downloads

1. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-23359

Tags

Computing Methodologies

Machine Learning

Machine Learning Approaches

Convolutional Neural Network

Facial Emotion Recognition.

Computer Sciences

Datavetenskap (datalogi)

Additional Fields

Identifer	oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-23359
Date	January 2022
Creators	Bejjagam, Lokesh, Chakradhara, Reshmi
Publisher	Blekinge Tekniska Högskola, Institutionen för datavetenskap
Source Sets	DiVA Archive at Upsalla University
Language	English
Detected Language	English
Type	Student thesis, info:eu-repo/semantics/bachelorThesis, text
Format	application/pdf
Rights	info:eu-repo/semantics/openAccess