A Comparison of Facial Landmark Detection Methods

dc.authoridEroglu Erdem, Cigdem/0000-0002-9264-5652
dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.authorwosidEroglu Erdem, Cigdem/Z-4276-2019
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.contributor.authorSandikci, Esra Nur
dc.contributor.authorEroglu Erdem, Cigdem
dc.contributor.authorUlukaya, Sezer
dc.date.accessioned2024-06-12T11:01:45Z
dc.date.available2024-06-12T11:01:45Z
dc.date.issued2018
dc.departmentTrakya Üniversitesien_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.description.abstractFace analysis is a rapidly developing research area and facial landmark detection is one of the pre-processing steps. In recent years, many algorithms and comprehensive survey/challenge papers have been published on facial landmark detection. In this work, we analysed six survey/challenge papers and observed that among open source systems deep learning (TCDCN, DCR) and regression based (CFSS) methods show superior performance.en_US
dc.description.sponsorshipIEEE,Huawei,Aselsan,NETAS,IEEE Turkey Sect,IEEE Signal Proc Soc,IEEE Commun Soc,ViSRATEK,Adresgezgini,Rohde & Schwarz,Integrated Syst & Syst Design,Atilim Univ,Havelsan,Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85050795129en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/21004
dc.identifier.wosWOS:000511448500210en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 26th Signal Processing And Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFace Analysisen_US
dc.subjectFacial Landmarksen_US
dc.subjectFace Alignmenten_US
dc.titleA Comparison of Facial Landmark Detection Methodsen_US
dc.typeConference Objecten_US

Dosyalar