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Reference TypeConference Paper (Conference Proceedings)
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyLimaBati:2018:SeImÍr
TitleSegmentação de Imagens de Íris Utilizando Deep Learning
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
Number of Files2
Size1052 KiB
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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
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2018-10-23 23:30:09 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
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.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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