How far did we get in face spoofing detection?

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dc.contributor.authorSouza, Luiz
dc.contributor.authorOliveira, Luciano
dc.contributor.authorPamplona, Mauricio
dc.contributor.authorPapa, Joao [UNESP]
dc.contributor.institutionUniversidade Federal da Bahia (UFBA)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:01:25Z
dc.date.available2018-11-26T16:01:25Z
dc.date.issued2018-06-01
dc.description.abstractThe growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.en
dc.description.affiliationUniv Fed Bahia, IVISION Lab, Salvador, BA, Brazil
dc.description.affiliationSao Paulo State Univ, RECOGNA Lab, Sao Paulo, Brazil
dc.description.affiliationUnespSao Paulo State Univ, RECOGNA Lab, Sao Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.format.extent368-381
dc.identifierhttp://dx.doi.org/10.1016/j.engappai.2018.04.013
dc.identifier.citationEngineering Applications Of Artificial Intelligence. Oxford: Pergamon-elsevier Science Ltd, v. 72, p. 368-381, 2018.
dc.identifier.doi10.1016/j.engappai.2018.04.013
dc.identifier.fileWOS000434239000031.pdf
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/11449/160335
dc.identifier.wosWOS:000434239000031
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofEngineering Applications Of Artificial Intelligence
dc.relation.ispartofsjr0,874
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectFace spoofing
dc.subjectFace recognition
dc.subjectSurvey
dc.subjectSpoofing attack
dc.titleHow far did we get in face spoofing detection?en
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.

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