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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3S4EE6B
Repositorysid.inpe.br/sibgrapi/2018/10.23.23.30
Last Update2018:10.23.23.30.09 (UTC) dieggo.filipe@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2018/10.23.23.30.09
Metadata Last Update2022:05.18.22.18.35 (UTC) administrator
Citation KeyLimaBati:2018:SeImÍr
TitleSegmentação de Imagens de Íris Utilizando Deep Learning
FormatOn-line
Year2018
Access Date2024, Apr. 28
Number of Files1
Size526 KiB
2. Context
Author1 Lima, Diego Filipe Souza de
2 Batista, Leonardo Vidal
Affiliation1 Federal University of Paraíba
2 Federal University of Paraíba
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 Addressdieggo.filipe@gmail.com
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 TypeUndergraduate Work
History (UTC)2018-10-23 23:30:09 :: dieggo.filipe@gmail.com -> administrator ::
2022-05-18 22:18:35 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsÍris
Segmentação
Deep Learning
Autoencoder
AbstractCurrent biometric systems can recognize individuals through various trait such as fingerprint, face, iris, palm, etc. Among these varied characteristics, the iris is one that most needs the collaboration of the individual. On the other hand, it is one of the most reliable forms of recognition because of the unique patterns it has in its composition. However, the use of this trait in a non-cooperative way means that the current systems perform below satisfactory, mainly due to the difficulty of locating and segmenting the iris region, which generates errors that are propagated throughout the recognition process, affecting the final performance of the systems directly. The present work proposes an iris segmentation algorithm using a Deep Learning technique known as Convolutional Autoencoder, which can perform satisfactorily in both cooperative and non-cooperative environments. The satisfactory performance of the algorithm was evident when compared to algorithms present in the literature, using images with similar capture patterns. The results of the segmentation process were evaluated using iris segmentation error and computational vision metrics, then compared with some of the best results found in the literature. The proposed method achieved in some cases an error rate 68% lower than the other algorithms.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Segmentação de Imagens...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3S4EE6B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S4EE6B
Languagept
Target FileSegmentação de Imagens de Íris Utilizando Deep Learning.pdf
User Groupdieggo.filipe@gmail.com
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 doi 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 versiontype volume


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