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Comparison of Gabor Filters and LBP Descriptors Applied to Spoofing Attack Detection in Facial Images

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Applied Informatics (ICAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1277))

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Abstract

Spoofing attack detection using facial images is a problem that violates the security of systems that use face recognition technologies. The objective of this research is to show a performance comparison between two texture descriptors: Gabor Filters and Local Binary Patterns applied to the spoofing detection by means of images of the face in order to provide information of interest for future research. These algorithms were evaluated under the same conditions. The results of experimentation show that Gabor filters obtain better discriminant descriptors in synthetic images, making them a good option for applying systems that use facial biometrics.

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Acknowledgements

We thanked to TecNM for the financial support provided through the project with the code 9091.20-P.

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Authors and Affiliations

  1. TecNM/Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca, Mexico

    Wendy Valderrama, Andrea Magadán, Raúl Pinto & José Ruiz

Authors
  1. Wendy Valderrama
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  2. Andrea Magadán
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  3. Raúl Pinto
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  4. José Ruiz
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Corresponding authors

Correspondence to Wendy Valderrama or Andrea Magadán .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Covenant University, Ota, Nigeria

    Sanjay Misra

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Valderrama, W., Magadán, A., Pinto, R., Ruiz, J. (2020). Comparison of Gabor Filters and LBP Descriptors Applied to Spoofing Attack Detection in Facial Images. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-61702-8_27

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  • Online ISBN: 978-3-030-61702-8

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