Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3S4EE6B |
Repository | sid.inpe.br/sibgrapi/2018/10.23.23.30 |
Last Update | 2018:10.23.23.30.09 dieggo.filipe@gmail.com |
Metadata | sid.inpe.br/sibgrapi/2018/10.23.23.30.09 |
Metadata Last Update | 2020:02.20.22.06.51 administrator |
Citation Key | LimaBati:2018:SeImÍr |
Title | Segmentação de Imagens de Íris Utilizando Deep Learning  |
Format | On-line |
Year | 2018 |
Date | Oct. 29 - Nov. 1, 2018 |
Access Date | 2021, Jan. 19 |
Number of Files | 2 |
Size | 1052 KiB |
Context area | |
Author | 1 Lima, Diego Filipe Souza de 2 Batista, Leonardo Vidal |
Affiliation | 1 Federal University of Paraíba 2 Federal University of Paraíba |
Editor | Ross, 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 Address | dieggo.filipe@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 31 (SIBGRAPI) |
Conference Location | Foz do Iguaçu, PR, Brazil |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Undergraduate Work |
History | 2018-10-23 23:30:09 :: dieggo.filipe@gmail.com -> administrator :: 2020-02-20 22:06:51 :: administrator -> :: 2018 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | Íris, Segmentação, Deep Learning, Autoencoder. |
Abstract | Current 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3S4EE6B |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3S4EE6B |
Language | pt |
Target File | Segmentação de Imagens de Íris Utilizando Deep Learning.pdf |
User Group | dieggo.filipe@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3RPADUS |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber contenttype 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 |
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