Identity statement area
Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyBezerraLaLuSeOlBrMe:2018:RoIrSe
TitleRobust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size2853 KiB
Context area
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
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-09-04 00:21:53 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
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.
source Directory Contentthere are no files
agreement Directory Content
agreement.html 03/09/2018 21:21 1.2 KiB 
Conditions of access and use area
Target File2018_SIBGRAPI_IrisSeg.pdf
Allied materials area
Next Higher Units8JMKD3MGPAW/3RPADUS
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume