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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RQEGQE
Repositorysid.inpe.br/sibgrapi/2018/09.10.22.41
Last Update2018:09.10.22.41.31 (UTC) iacopoma@usc.edu
Metadata Repositorysid.inpe.br/sibgrapi/2018/09.10.22.41.31
Metadata Last Update2022:05.18.22.18.30 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00067
Citation KeyMasiWuHassNata:2018:DeFaRe
TitleDeep Face Recognition: a Survey
FormatOn-line
Year2018
Access Date2024, May 06
Number of Files1
Size395 KiB
2. Context
Author1 Masi, Iacopo
2 Wu, Yue
3 Hassner, Tal
4 Natarajan, Prem
Affiliation1 Information Sciences Institute (ISI), University of Southern California (USC)
2 Information Sciences Institute (ISI), University of Southern California (USC)
3 The Open University of Israel, Raanana, Israel
4 Information Sciences Institute (ISI), University of Southern California (USC)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressiacopoma@usc.edu
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2018-09-10 22:41:31 :: iacopoma@usc.edu -> administrator ::
2022-05-18 22:18:30 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsface recognition
deep learning
survey
AbstractFace recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Deep Face Recognition:...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RQEGQE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RQEGQE
Languageen
Target FilePID5564503.pdf
User Groupiacopoma@usc.edu
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 8
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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