Deep Representations For Iris, Face, And Fingerprint Spoofing Detection

dc.creatorMenotti
dc.creatorDavid; Chiachia
dc.creatorGiovani; Pinto
dc.creatorAllan; Schwartz
dc.creatorWilliam Robson; Pedrini
dc.creatorHelio; Falcao
dc.creatorAlexandre Xavier; Rocha
dc.creatorAnderson
dc.date2015-APR
dc.date2016-06-07T13:33:29Z
dc.date2016-06-07T13:33:29Z
dc.date.accessioned2018-03-29T01:49:13Z
dc.date.available2018-03-29T01:49:13Z
dc.identifier
dc.identifierDeep Representations For Iris, Face, And Fingerprint Spoofing Detection. Ieee-inst Electrical Electronics Engineers Inc, v. 10, p. 864-879 APR-2015.
dc.identifier1556-6013
dc.identifierWOS:000351753400008
dc.identifier10.1109/TIFS.2015.2398817
dc.identifierhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7029061
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/243711
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1307409
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionBiometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks-each one containing real and fake samples of a given biometric modality and attack type- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
dc.description10
dc.description4
dc.description
dc.description864
dc.description879
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFederal University of Ouro Preto through Brazilian National Research Council [303673/2010-9, 304352/2012-8, 307113/2012-4, 477662/2013-7, 487529/2013-8, 479070/2013-0, 477457/2013-4]
dc.descriptionMinas Gerais Research Foundation [APQ-01806-13]
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description
dc.description
dc.description
dc.languageen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.publisher
dc.publisherPISCATAWAY
dc.relationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
dc.rightsfechado
dc.sourceWOS
dc.subjectLiveness Detection
dc.subjectTexture Classification
dc.subjectContact-lenses
dc.subjectRecognition
dc.subjectImage
dc.subjectSearch
dc.subjectModel
dc.titleDeep Representations For Iris, Face, And Fingerprint Spoofing Detection
dc.typeArtículos de revistas


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