서브메뉴
검색
Learning-Based Facial Attribute Estimation and Manipulation
Learning-Based Facial Attribute Estimation and Manipulation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151502
- ISBN
- 9798383215944
- DDC
- 621.3
- 저자명
- Jin, Shiwei.
- 서명/저자
- Learning-Based Facial Attribute Estimation and Manipulation
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 128 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Nguyen, Truong.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Facial attribute analysis plays a crucial role in various fields such as surveillance, entertainment, healthcare, and human-computer interaction. The advert of deep neural networks has sparked a growing interest in learning-based facial attribute analysis. This dissertation focuses on learning-based facial attribute analysis, encompassing facial attribute estimation and manipulation tasks. We focused on solving challenges including addressing data scarcity in supervised facial attribute estimation, handling facial attribute manipulation in high-resolution images, efficiently disentangling targeted attributes from others, and training a facial attribute manipulator with datasets from a small number of subjects.The dissertation is structured around three key facial attribute categories: head orientations, gaze directions, and facial action units. The first part delves into improving appearance-based gaze estimation by considering person-dependent anatomical variations and accounting for ocular countering-rolling (OCR) responses, resulting in a more efficient and accurate method. The second part introduces ReDirTrans, a portable network designed for gaze redirection in high-resolution face images. By focusing on latent-to-latent translation, ReDirTrans enables precise gaze and head pose redirection while preserving other attributes, expanding its applicability beyond limited ranges of faces. The final part presents AUEditNet, a model for manipulating facial action unit intensities. This addresses challenges posed by data scarcity by effectively disentangling attributes and identity within a limited subject pool. AUEditNet demonstrates superior accuracy in editing AU intensities across 12 AUs, showcasing its potential for fine-grained facial attribute manipulation.Overall, this dissertation contributes novel methodologies in learning-based facial attribute analysis, paving the way for enhanced performance and versatility across various real-world applications.
- 일반주제명
- Computer engineering
- 일반주제명
- Engineering
- 일반주제명
- Electrical engineering
- 키워드
- Deep learning
- 키워드
- Image synthesis
- 키워드
- Image processing
- 기타저자
- University of California, San Diego Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017161919
■00520250211151502
■006m o d
■007cr#unu||||||||
■020 ▼a9798383215944
■035 ▼a(MiAaPQ)AAI31298051
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aJin, Shiwei.
■24510▼aLearning-Based Facial Attribute Estimation and Manipulation
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a128 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Nguyen, Truong.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aFacial attribute analysis plays a crucial role in various fields such as surveillance, entertainment, healthcare, and human-computer interaction. The advert of deep neural networks has sparked a growing interest in learning-based facial attribute analysis. This dissertation focuses on learning-based facial attribute analysis, encompassing facial attribute estimation and manipulation tasks. We focused on solving challenges including addressing data scarcity in supervised facial attribute estimation, handling facial attribute manipulation in high-resolution images, efficiently disentangling targeted attributes from others, and training a facial attribute manipulator with datasets from a small number of subjects.The dissertation is structured around three key facial attribute categories: head orientations, gaze directions, and facial action units. The first part delves into improving appearance-based gaze estimation by considering person-dependent anatomical variations and accounting for ocular countering-rolling (OCR) responses, resulting in a more efficient and accurate method. The second part introduces ReDirTrans, a portable network designed for gaze redirection in high-resolution face images. By focusing on latent-to-latent translation, ReDirTrans enables precise gaze and head pose redirection while preserving other attributes, expanding its applicability beyond limited ranges of faces. The final part presents AUEditNet, a model for manipulating facial action unit intensities. This addresses challenges posed by data scarcity by effectively disentangling attributes and identity within a limited subject pool. AUEditNet demonstrates superior accuracy in editing AU intensities across 12 AUs, showcasing its potential for fine-grained facial attribute manipulation.Overall, this dissertation contributes novel methodologies in learning-based facial attribute analysis, paving the way for enhanced performance and versatility across various real-world applications.
■590 ▼aSchool code: 0033.
■650 4▼aComputer engineering
■650 4▼aEngineering
■650 4▼aElectrical engineering
■653 ▼aDeep learning
■653 ▼aFacial attribute editing
■653 ▼aImage synthesis
■653 ▼aImage processing
■690 ▼a0464
■690 ▼a0544
■690 ▼a0537
■71020▼aUniversity of California, San Diego▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161919▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
