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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RPB5DL
Repositorysid.inpe.br/sibgrapi/2018/09.04.00.21
Last Update2018:09.04.00.21.53 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/09.04.00.21.53
Metadata Last Update2022:06.14.00.09.23 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00043
Citation KeyBezerraLaLuSeOlBrMe:2018:RoIrSe
TitleRobust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
FormatOn-line
Year2018
Access Date2024, Sep. 13
Number of Files1
Size2853 KiB
2. Context
Author1 Bezerra, Cides
2 Laroca, Rayson
3 Lucio, Diego R.
4 Severo, Evair
5 Oliveira, Lucas F.
6 Britto Jr, Alceu S.
7 Menotti, David
Affiliation1 Federal University of Parana
2 Federal University of Parana
3 Federal University of Parana
4 Federal University of Parana
5 Federal University of Parana
6 Pontifical Catholic University of Parana
7 Federal University of Parana
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 Addresscides.bezerra@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-09-04 00:21:53 :: cides.bezerra@gmail.com -> administrator ::
2022-06-14 00:09:23 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsBiometric
Iris segmentation
Non-cooperative
AbstractThe iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Robust Iris Segmentation...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Robust Iris Segmentation...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RPB5DL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RPB5DL
Languageen
Target File2018_SIBGRAPI_IrisSeg.pdf
User Groupcides.bezerra@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 24
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
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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