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

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Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

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Abstract

Fingerprint-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.

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Notes

  1. 1.

    The CrossMatch sensor has a cataloging error, so we do not consider it in the evaluations.

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Acknowledgments

This study was financed in part by the São Paulo Research Foundation (FAPESP), process #15/14358-0, by the Brazilian National Council for Scientific and Technological Development (CNPq), process #381991/2020-2, and by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil” (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. University of São Paulo, São Carlos, SP, 13566-590, Brazil

    Rodrigo Colnago Contreras & Luis Gustavo Nonato

  2. São Paulo State University, São José do Rio Preto, SP, 15054-000, Brazil

    Maurílio Boaventura & Inês Aparecida Gasparotto Boaventura

  3. New York University, New York, NY, 10012, USA

    Bruno Gomes Coelho

  4. Federal University of São Carlos, São Carlos, SP, 13565-905, Brazil

    Monique Simplicio Viana

Authors
  1. Rodrigo Colnago Contreras
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  2. Luis Gustavo Nonato
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  3. Maurílio Boaventura
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  4. Inês Aparecida Gasparotto Boaventura
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  6. Monique Simplicio Viana
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Corresponding author

Correspondence to Rodrigo Colnago Contreras .

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Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Contreras, R.C., Nonato, L.G., Boaventura, M., Boaventura, I.A.G., Coelho, B.G., Viana, M.S. (2021). A New Multi-filter Framework with Statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systems. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_39

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  • DOI: https://doi.org/10.1007/978-3-030-87897-9_39

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