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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3MD4NJB
Repositorysid.inpe.br/sibgrapi/2016/09.06.18.06
Last Update2016:09.06.18.06.25 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/09.06.18.06.25
Metadata Last Update2022:05.18.22.21.10 (UTC) administrator
Citation KeyFilisbinoGiraThom:2016:RaEiTh
TitleRanking Eigenfaces Through Adaboost and Perceptron Ensembles
FormatOn-line
Year2016
Access Date2024, Apr. 28
Number of Files1
Size416 KiB
2. Context
Author1 Filisbino, Tiene Andre
2 Giraldi, Gilson Antonio
3 Thomaz, Carlos Eduardo
Affiliation1 Laboratorio Nacional de Comnputação Científica
2 Laboratorio Nacional de Comnputação Científica
3 Centro Universitário da FEI
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 Addresstiene@lncc.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)2016-09-06 18:06:25 :: tiene@lncc.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
KeywordsRanking PCA Components
Separating Hyperplanes
Perceptron
AdaBoost
Face Image Analysis
AbstractThe fact that principal component analysis (PCA) does not necessarily represent important discriminant directions to separate sample groups motivates the development of the multi-class discriminant principal component analysis (MDPCA). This technique addresses the problem of ranking face features in N-class problems computed by PCA components (eigenfaces). Given a database, the MDPCA builds a linear support vector machine (SVM) ensemble to get the separating hyperplanes that are combined through an AdaBoost technique to determine the discriminant contribution of each PCA feature. In this paper, we follow the MDPCA methodology but we replace the SVM by the linear perceptron as the basic learner in the AdaBoost approach. In the computational experiments we compare the obtained technique, called MDPCA-Perceptron, with the PCA and the original MDPCA through facial expression experiments. Our computational results have shown that the principal components selected by the MDPCA-Perceptron allow competitive recognition rates in lower dimensional spaces with promising results for reconstruction tasks as well.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3MD4NJB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3MD4NJB
Languageen
Target FileREAPE2.pdf
User Grouptiene@lncc.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 7
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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