Digital makeup transfer using machine learning algorithms

As the world becomes more and more interconnected, social media has become a popular way for people to share their lives and connect with others. Posting self-portraitures have become increasingly popular on social media platforms, leading to a rise in demand for photo-editing applications as a way...

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Main Author: Lim, Joey Soo Yee
Other Authors: He Ying
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/165951
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1659512023-04-14T15:37:30Z Digital makeup transfer using machine learning algorithms Lim, Joey Soo Yee He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering As the world becomes more and more interconnected, social media has become a popular way for people to share their lives and connect with others. Posting self-portraitures have become increasingly popular on social media platforms, leading to a rise in demand for photo-editing applications as a way to beautify their self-portraits. However, current applications available for photo editing are designed solely for raster images, which can result in a loss in image quality when resized. Hence, this project presents the design and implementation of a digital makeup transfer algorithm using machine learning algorithms which will allow users to apply makeup to their portrait images digitally. This project aims to develop a makeup transfer algorithm that edits vector graphics such that image quality is maintained. Different colour transfer methods and facial segmentation models will also be examined and further discussed in this project Bachelor of Science in Data Science and Artificial Intelligence 2023-04-14T09:51:48Z 2023-04-14T09:51:48Z 2023 Final Year Project (FYP) Lim, J. S. Y. (2023). Digital makeup transfer using machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165951 https://hdl.handle.net/10356/165951 en SCSE22-0135 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
Lim, Joey Soo Yee
Digital makeup transfer using machine learning algorithms
description As the world becomes more and more interconnected, social media has become a popular way for people to share their lives and connect with others. Posting self-portraitures have become increasingly popular on social media platforms, leading to a rise in demand for photo-editing applications as a way to beautify their self-portraits. However, current applications available for photo editing are designed solely for raster images, which can result in a loss in image quality when resized. Hence, this project presents the design and implementation of a digital makeup transfer algorithm using machine learning algorithms which will allow users to apply makeup to their portrait images digitally. This project aims to develop a makeup transfer algorithm that edits vector graphics such that image quality is maintained. Different colour transfer methods and facial segmentation models will also be examined and further discussed in this project
author2 He Ying
author_facet He Ying
Lim, Joey Soo Yee
format Final Year Project
author Lim, Joey Soo Yee
author_sort Lim, Joey Soo Yee
title Digital makeup transfer using machine learning algorithms
title_short Digital makeup transfer using machine learning algorithms
title_full Digital makeup transfer using machine learning algorithms
title_fullStr Digital makeup transfer using machine learning algorithms
title_full_unstemmed Digital makeup transfer using machine learning algorithms
title_sort digital makeup transfer using machine learning algorithms
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/165951
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