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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3ME7N65
Repositorysid.inpe.br/sibgrapi/2016/09.13.14.02
Last Update2016:09.13.14.02.37 (UTC) flavio@ufpr.br
Metadata Repositorysid.inpe.br/sibgrapi/2016/09.13.14.02.37
Metadata Last Update2022:05.18.22.21.10 (UTC) administrator
Citation KeyZavanNascBellSilv:2016:CoLaMe
TitleNosePose: a competitive, landmark-free methodology for head pose estimation in the wild
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size4120 KiB
2. Context
Author1 Zavan, Flávio Henrique de Bittencourt
2 Nascimento, Antônio Carlos Paes
3 Bellon, Olga Regina Pereira
4 Silva, Luciano
Affiliation1 Universidade Federal do Paraná
2 Universidade Federal do Paraná
3 Universidade Federal do Paraná
4 Universidade Federal do Paraná
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressflavio@ufpr.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeFace Processing Application Paper
History (UTC)2016-09-13 14:02:37 :: flavio@ufpr.br -> administrator ::
2022-05-18 22:21:10 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsHead pose estimation
Nose pose estimation
Face image analysis
Support vector machines
Convolutional neural network
AbstractWe perform head pose estimation solely based on the nose region as input, extracted from 2D images in unconstrained environments. Such information is useful for many face analysis applications, such as recognition, reconstruction, alignment, tracking and expression recognition. Using the nose region has advantages over using the whole face; not only it is less likely to be occluded by acesssories, it is also visible and proved to be highly discriminant in all poses from profile to frontal. To this end, we propose and compare two different approaches, based on Support Vector Machines (SVM-NosePose) and on Convolutional Neural Networks (CNN-NosePose) such that no landmarks are needed to perform pose estimation, favoring success in extreme pose and environment where landmark detection is non-trivial. Our NosePose methodology was applied to four publicly available uncontrolled image datasets (McGillFaces, AFW, PaSC and IJB-A). Results show that both SVM-NosePose and CNN-NosePose approaches are competitive, through thoughtful and comprehensive experiments, when compared against state-of-the-art works on head pose estimation.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > NosePose: a competitive,...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3ME7N65
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3ME7N65
Languageen
Target Filenose_pose_camera_ready.pdf
User Groupflavio@ufpr.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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