Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JLT3D2
Repositorysid.inpe.br/sibgrapi/2015/06.14.15.54
Last Update2015:06.14.15.54.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.14.15.54.29
Metadata Last Update2022:06.14.00.08.01 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.16
Citation KeySilvaLuBaPeFaMe:2015:ApIrCo
TitleAn Approach to Iris Contact Lens Detection based on Deep Image Representations
FormatOn-line
Year2015
Access Date2024, Apr. 28
Number of Files1
Size3104 KiB
2. Context
Author1 Silva, Pedro
2 Luz, Eduardo
3 Baeta, Rafael
4 Pedrini, Helio
5 Falcao, Alexandre Xavier
6 Menotti, David
Affiliation1 Federal University of Ouro Preto
2 Federal University of Ouro Preto
3 Federal University of Ouro Preto
4 University of Campinas
5 University of Campinas
6 Federal University of Ouro Preto
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressmenottid@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-14 15:54:29 :: menottid@gmail.com -> administrator ::
2022-06-14 00:08:01 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsbiometrics
contact lens detection
deep learning
convolutional networks
AbstractSpoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with softmax regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > An Approach to...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > An Approach to...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 14/06/2015 12:54 0.7 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JLT3D2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JLT3D2
Languageen
Target FilePID3758179.pdf
User Groupmenottid@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 7
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


Close