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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMP3CL
Repositorysid.inpe.br/sibgrapi/2015/06.19.21.39
Last Update2015:06.19.21.39.23 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.19.21.39.23
Metadata Last Update2022:06.14.00.08.12 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.14
Citation KeyLopesAguiOliv:2015:FaExRe
TitleA Facial Expression Recognition System Using Convolutional Networks
FormatOn-line
Year2015
Access Date2024, Apr. 28
Number of Files1
Size1714 KiB
2. Context
Author1 Lopes, Andre Teixeira
2 Aguiar, Edilson de
3 Oliveira-Santos, Thiago
Affiliation1 Universidade Federal do Espírito Santo
2 Universidade Federal do Espírito Santo
3 Universidade Federal do Espírito Santo
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressandreteixeiralopes@hotmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 21:39:23 :: andreteixeiralopes@hotmail.com -> administrator ::
2022-06-14 00:08:12 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsExpression
Convolutional Networks
Computer Vision
Machine Learning
Expression Specific Features
AbstractFacial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > A Facial Expression...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Facial Expression...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMP3CL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMP3CL
Languageen
Target FilePID3755347.pdf
User Groupandreteixeiralopes@hotmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 6
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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