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dc.contributor.authorDe Souza, Gustavo Botelho
dc.contributor.authorDa Silva Santos, Daniel Felipe [UNESP]
dc.contributor.authorPires, Rafael Goncalves
dc.contributor.authorMarana, Aparecido Nilceu [UNESP]
dc.contributor.authorPapa, Joao Paulo [UNESP]
dc.date.accessioned2018-12-11T17:34:42Z
dc.date.available2018-12-11T17:34:42Z
dc.date.issued2017-12-01
dc.identifierhttp://dx.doi.org/10.1109/TCSII.2017.2764460
dc.identifier.citationIEEE Transactions on Circuits and Systems II: Express Briefs, v. 64, n. 12, p. 1397-1401, 2017.
dc.identifier.issn1549-7747
dc.identifier.urihttp://hdl.handle.net/11449/179319
dc.description.abstractBiometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.en
dc.format.extent1397-1401
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Express Briefs
dc.sourceScopus
dc.subjectbiometrics
dc.subjectconvolutional neural networks
dc.subjectdeep texture features
dc.subjectFace recognition
dc.subjectspoofing detection
dc.titleDeep Texture Features for Robust Face Spoofing Detectionen
dc.typeArtigo
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationCCET-Exact and Technology Sciences Center Federal University of São Carlos
dc.description.affiliationDepartment of Computing Faculty of Sciences UNESP-São Paulo State University
dc.description.affiliationUnespDepartment of Computing Faculty of Sciences UNESP-São Paulo State University
dc.identifier.doi10.1109/TCSII.2017.2764460
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-85032672861
dc.identifier.file2-s2.0-85032672861.pdf
unesp.author.orcid0000-0002-4441-9108[1]
unesp.author.orcid0000-0003-4861-7061[4]
unesp.author.orcid0000-0002-6494-7514[5]
dc.relation.ispartofsjr0,758
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