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Detection of Presentation Attacks on Facial Authentication Systems Using Intel RealSense Depth Cameras

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Hybrid Intelligent Systems (HIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 647))

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Abstract

In this paper, we explore the prospects for using Intel RealSense depth cameras to solve the problem of presentation attack detection in facial authentication systems. Studies known to date use quantized depth data. Such an approach makes it impossible to connect these data with anthropometric facial features and scene geometry. In addition, in recent papers, some researchers declared the limitation of using depth cameras in this problem only at small (less than 1 m) distances to the face. In this regard, we have made our collection of 480 samples containing bonafide images and two types of presentation attacks. The samples were taken at distances up to 2 m, as well as in different face positions relative to the camera axis. We also designed a set of features based on texture analysis, anthropometric properties, and face oval localization. The conducted experiments show the advantage of using the original, not quantized depth data. Our study also refutes the thesis that depth cameras can be used for presentation attack detection only at short distances. Additionally, we demonstrate the applicability of the proposed features on one of the publicly available datasets.

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Notes

  1. 1.

    https://github.com/vicanfed/depth-pad-dataset.

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Acknowledgments

This work was supported by the Russian Foundation for Basic Research (project 19-29-09045).

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Authors and Affiliations

  1. Samara National Research University, Moskovskoe shosse, 34, Samara, 443086, Russia

    A. A. Tarasov, A. Y. Denisova & V. A. Fedoseev

  2. IPSI RAS—Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, Samara, 443001, Russia

    A. Y. Denisova & V. A. Fedoseev

Authors
  1. A. A. Tarasov
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  2. A. Y. Denisova
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  3. V. A. Fedoseev
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Corresponding author

Correspondence to A. Y. Denisova .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Symbiosis International University, Pune, India

    Ketan Kotecha

  4. University of **an, **an, China

    Kun Ma

  5. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Mala, Kerala, India

    Pooja Manghirmalani Mishra

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Tarasov, A.A., Denisova, A.Y., Fedoseev, V.A. (2023). Detection of Presentation Attacks on Facial Authentication Systems Using Intel RealSense Depth Cameras. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_119

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