A New Multi-filter Framework with Statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systems

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dc.contributor.authorContreras, Rodrigo Colnago
dc.contributor.authorNonato, Luis Gustavo
dc.contributor.authorBoaventura, Maurílio [UNESP]
dc.contributor.authorBoaventura, Inês Aparecida Gasparotto [UNESP]
dc.contributor.authorCoelho, Bruno Gomes
dc.contributor.authorViana, Monique Simplicio
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionNew York University
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2022-05-01T09:47:27Z
dc.date.available2022-05-01T09:47:27Z
dc.date.issued2021-01-01
dc.description.abstractFingerprint-based authentication systems represent what is most common in biometric authentication systems. Today’s simplest tasks, such as unlocking functions on a personal cell phone, may require its owner’s fingerprint. However, along with the advancement of this category of systems, have emerged fraud strategies that aim to guarantee undue access to illegitimate individuals. In this case, one of the most common frauds is that in which the impostor presents manufactured biometry, or spoofing, to the system, simulating the biometry of another user. In this work, we propose a new framework that makes two filtered versions of the fingerprint image in order to increase the amount of information that can be useful in the process of detecting fraud in fingerprint images. Besides, we propose a new texture descriptor based on the well-known dense Scale-Invariant Feature Transform (SIFT): the statistical dense SIFT, in which their descriptors are summarized using a set of signal processing functions. The proposed methodology is evaluated in benchmarks of two editions of LivDet competitions, assuming competitive results in comparison to techniques that configure the state of the art of the problem.en
dc.description.affiliationUniversity of São Paulo
dc.description.affiliationSão Paulo State University
dc.description.affiliationNew York University
dc.description.affiliationFederal University of São Carlos
dc.description.affiliationUnespSão Paulo State University
dc.format.extent442-455
dc.identifierhttp://dx.doi.org/10.1007/978-3-030-87897-9_39
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12855 LNAI, p. 442-455.
dc.identifier.doi10.1007/978-3-030-87897-9_39
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85117730719
dc.identifier.urihttp://hdl.handle.net/11449/233733
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.subjectDense SIFT
dc.subjectFingerprint authentication system
dc.subjectLiveness detection
dc.subjectPattern recognition
dc.subjectSpoofing detection
dc.titleA New Multi-filter Framework with Statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systemsen
dc.typeTrabalho apresentado em evento
unesp.author.orcid0000-0003-4003-7791[1]
unesp.author.orcid0000-0002-8514-8033[2]
unesp.author.orcid0000-0003-3936-5894[3]
unesp.author.orcid0000-0002-6422-2660[4]
unesp.author.orcid0000-0002-3093-9217[5]
unesp.author.orcid0000-0002-2960-8293[6]

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