Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWu, Rongliangen_US
dc.identifier.citationWu, R. (2023). Deep learning for facial expression editing. Doctoral thesis, Nanyang Technological University, Singapore.
dc.description.abstractIn this day and age of digital media, facial expression editing, which aims to transform the facial expression of a source facial image to a desired one without changing the face identity, has attracted increasing interest from both academia and industrial communities due to its wide applications in many tasks. Automatic facial expression editing has been explored extensively with the prevalence of generative adversarial networks in recent years. Although some research works have been reported and achieved very promising progress, the task of facial expression editing is still facing four major challenges, including the unsatisfactory editing quality issue, the constrained data annotation issue, the limited controllability issue and the multi-modality issue. This thesis focuses on the above-mentioned challenges in facial expression editing task and introduces several novel deep-learning-based techniques to alleviate the corresponding challenges. Extensive experiments show that the proposed approaches achieve superior performance in facial expression editing.en_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeep learning for facial expression editingen_US
dc.typeThesis-Doctor of Philosophyen_US
dc.contributor.supervisorLu Shijianen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeDoctor of Philosophyen_US
item.fulltextWith Fulltext-
Appears in Collections:SCSE Theses
Files in This Item:
File Description SizeFormat 
Amended_Thesis.pdfThesis4.45 MBAdobe PDFThumbnail

Page view(s)

Updated on Jul 18, 2024

Download(s) 50

Updated on Jul 18, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.