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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFBDPL
Repositorysid.inpe.br/sibgrapi/2017/08.18.17.27
Last Update2017:08.18.17.27.45 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.18.17.27.45
Metadata Last Update2022:06.14.00.08.48 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.60
Citation KeyZavarezBerrOliv:2017:CrFaEx
TitleCross-Database Facial Expression Recognition Based on Fine-Tuned Deep Convolutional Network
FormatOn-line
Year2017
Access Date2024, Apr. 28
Number of Files1
Size436 KiB
2. Context
Author1 Zavarez, Marcus Vinicius
2 Berriel, Rodrigo F.
3 Oliveira-Santos, Thiago
Affiliation1 Universidade Federal do Espirito Santo
2 Universidade Federal do Espirito Santo
3 Universidade Federal do Espirito Santo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressvini.vni@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-18 17:27:45 :: vini.vni@gmail.com -> administrator ::
2022-06-14 00:08:48 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsFacial expression recognition
convolutional neural network
deep learning
cross-database
AbstractFacial expression recognition is a very important research field to understand human emotions. Many facial expression recognition systems have been proposed in the literature over the years. Some of these methods use neural network approaches with deep architectures to address the problem. Although it seems that the facial expression recognition problem has been solved, there is a large difference between the results achieved using the same database to train and test the network and the cross-database protocol. In this paper, we extensively investigate the performance influence of fine-tuning with cross-database approach. In order to perform the study, the VGG-Face Deep Convolutional Network model (pre-trained for face recognition) was fine-tuned to recognize facial expressions considering different well-established databases in the literature: CK+, JAFFE, MMI, RaFD, KDEF, BU3DFE, and AR Face. The cross-database experiments were organized so that one of the databases was separated as test set and the others as training, and each experiment was ran multiple times to ensure the results. Our results show a significant improvement on the use of pre-trained models against randomly initialized Convolutional Neural Networks on the facial expression recognition problem, for example achieving 88.58%, 67.03%, 85.97%, and 72.55% average accuracy testing in the CK+, MMI, RaFD, and KDEF, respectively. Additionally, in absolute terms, the results show an improvement in the literature for cross-database facial expression recognition with the use of pre-trained models.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Cross-Database Facial Expression...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Cross-Database Facial Expression...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFBDPL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFBDPL
Languageen
Target FilePaperSib2017.pdf
User Groupvini.vni@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 7
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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