Using Visual Rhythms For Detecting Video-based Facial Spoof Attacks

dc.creatorPinto
dc.creatorAllan; Schwartz
dc.creatorWilliam Robson; Pedrini
dc.creatorHelio; Rocha
dc.creatorAnderson de Rezende
dc.date2015-MAY
dc.date2016-06-07T13:17:40Z
dc.date2016-06-07T13:17:40Z
dc.date.accessioned2018-03-29T01:38:13Z
dc.date.available2018-03-29T01:38:13Z
dc.identifier
dc.identifierUsing Visual Rhythms For Detecting Video-based Facial Spoof Attacks. Ieee-inst Electrical Electronics Engineers Inc, v. 10, p. 1025-1038 MAY-2015.
dc.identifier1556-6013
dc.identifierWOS:000352534300011
dc.identifier10.1109/TIFS.2015.2395139
dc.identifierhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7017526
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/242379
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1306077
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionSpoofing attacks or impersonation can be easily accomplished in a facial biometric system wherein users without access privileges attempt to authenticate themselves as valid users, in which an impostor needs only a photograph or a video with facial information of a legitimate user. Even with recent advances in biometrics, information forensics and security, vulnerability of facial biometric systems against spoofing attacks is still an open problem. Even though several methods have been proposed for photo-based spoofing attack detection, attacks performed with videos have been vastly overlooked, which hinders the use of the facial biometric systems in modern applications. In this paper, we present an algorithm for video-based spoofing attack detection through the analysis of global information which is invariant to content, since we discard video contents and analyze content-independent noise signatures present in the video related to the unique acquisition processes. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access videos. For that, we use the Fourier spectrum followed by the computation of video visual rhythms and the extraction of different characterization methods. For evaluation, we consider the novel unicamp video-attack database, which comprises 17 076 videos composed of real access and spoofing attack videos. In addition, we evaluate the proposed method using the replay-attack database, which contains photo-based and video-based face spoofing attacks.
dc.description10
dc.description5
dc.description
dc.description1025
dc.description1038
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionNational Council for Scientific and Technological Development [304352/2012-8, 477662/2013-7]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.descriptionMicrosoft
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFAPESP [2010/05647-4, 2011/22749-8]
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.subjectFace
dc.subjectClassification
dc.subjectFeatures
dc.subjectTexture
dc.subjectImages
dc.titleUsing Visual Rhythms For Detecting Video-based Facial Spoof Attacks
dc.typeArtículos de revistas


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