Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168477
Title: Deep learning for facial expression editing
Authors: Wu, Rongliang
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Wu, R. (2023). Deep learning for facial expression editing. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168477
Abstract: In 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.
URI: https://hdl.handle.net/10356/168477
DOI: 10.32657/10356/168477
Schools: School of Computer Science and Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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