Facial expression recognition by deep learning
Facial expressions have been proven to be a key element in social interaction. With the increasing popularity of artificial intelligence, facial expression systems using various methods have been designed and studied. Traditional machine learning methods such as support vector machines are widely us...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/141030 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-141030 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1410302023-07-07T17:46:40Z Facial expression recognition by deep learning Ding, Hong Wei Jiang Xudong School of Electrical and Electronic Engineering exdjiang@ntu.edu.sg Engineering::Electrical and electronic engineering Facial expressions have been proven to be a key element in social interaction. With the increasing popularity of artificial intelligence, facial expression systems using various methods have been designed and studied. Traditional machine learning methods such as support vector machines are widely used in this field. However, most of the traditional machine learning methods require a lot of domain expertise as features need to be identified manually. In contrast, deep learning makes use of network layers to learn features hierarchically by itself. Therefore, this project is to study and develop a facial expression recognition system using convolutional neural network. In this report, the way convolutional neural network works, datasets used (the Extended Cohn-Kanade Dataset), neural network used (VGG net), implementation of the system, design of graphical user interface and future works are discussed. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-03T07:59:37Z 2020-06-03T07:59:37Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141030 en application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering |
spellingShingle |
Engineering::Electrical and electronic engineering Ding, Hong Wei Facial expression recognition by deep learning |
description |
Facial expressions have been proven to be a key element in social interaction. With the increasing popularity of artificial intelligence, facial expression systems using various methods have been designed and studied. Traditional machine learning methods such as support vector machines are widely used in this field. However, most of the traditional machine learning methods require a lot of domain expertise as features need to be identified manually. In contrast, deep learning makes use of network layers to learn features hierarchically by itself. Therefore, this project is to study and develop a facial expression recognition system using convolutional neural network. In this report, the way convolutional neural network works, datasets used (the Extended Cohn-Kanade Dataset), neural network used (VGG net), implementation of the system, design of graphical user interface and future works are discussed. |
author2 |
Jiang Xudong |
author_facet |
Jiang Xudong Ding, Hong Wei |
format |
Final Year Project |
author |
Ding, Hong Wei |
author_sort |
Ding, Hong Wei |
title |
Facial expression recognition by deep learning |
title_short |
Facial expression recognition by deep learning |
title_full |
Facial expression recognition by deep learning |
title_fullStr |
Facial expression recognition by deep learning |
title_full_unstemmed |
Facial expression recognition by deep learning |
title_sort |
facial expression recognition by deep learning |
publisher |
Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/141030 |
_version_ |
1772826610588712960 |