Digital makeup using machine learning algorithms
In this increasingly digitalised world, the use of social media has been on the rise. Users widely upload their portrait or self-portrait photographs on social media platforms to share with their closed ones, and many would tend to edit their photographs before sharing them. Such edits that users ca...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/153340 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-153340 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1533402021-11-23T01:46:46Z Digital makeup using machine learning algorithms Wong, Zhen Yan He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering In this increasingly digitalised world, the use of social media has been on the rise. Users widely upload their portrait or self-portrait photographs on social media platforms to share with their closed ones, and many would tend to edit their photographs before sharing them. Such edits that users carry out are done using general purpose photo editing tools to apply filters or makeover applications to beautify their faces. In this project, a digital makeup algorithm will be developed using machine learning algorithms to allow users to digitally apply makeup to their portrait images. This aims to reduce the amount of time and effort needed for users to polish facial images. Additionally, this project will look into the different methods of colour transfer and the implementation details of the digital makeup algorithm. Bachelor of Engineering (Computer Science) 2021-11-23T01:38:23Z 2021-11-23T01:38:23Z 2021 Final Year Project (FYP) Wong, Z. Y. (2021). Digital makeup using machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153340 https://hdl.handle.net/10356/153340 en SCSE20-0670 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::Computer science and engineering |
spellingShingle |
Engineering::Computer science and engineering Wong, Zhen Yan Digital makeup using machine learning algorithms |
description |
In this increasingly digitalised world, the use of social media has been on the rise. Users widely upload their portrait or self-portrait photographs on social media platforms to share with their closed ones, and many would tend to edit their photographs before sharing them. Such edits that users carry out are done using general purpose photo editing tools to apply filters or makeover applications to beautify their faces. In this project, a digital makeup algorithm will be developed using machine learning algorithms to allow users to digitally apply makeup to their portrait images. This aims to reduce the amount of time and effort needed for users to polish facial images. Additionally, this project will look into the different methods of colour transfer and the implementation details of the digital makeup algorithm. |
author2 |
He Ying |
author_facet |
He Ying Wong, Zhen Yan |
format |
Final Year Project |
author |
Wong, Zhen Yan |
author_sort |
Wong, Zhen Yan |
title |
Digital makeup using machine learning algorithms |
title_short |
Digital makeup using machine learning algorithms |
title_full |
Digital makeup using machine learning algorithms |
title_fullStr |
Digital makeup using machine learning algorithms |
title_full_unstemmed |
Digital makeup using machine learning algorithms |
title_sort |
digital makeup using machine learning algorithms |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/153340 |
_version_ |
1718368055346593792 |