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Presentation Attack Detection for Face in Mobile Phones

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Selfie Biometrics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Face is the most accessible biometric modality which can be used for identity verification in mobile phone applications, and it is vulnerable to many different presentation attacks, such as using a printed face/digital screen face to access the mobile phone. Presentation attack detection is a very critical step before feeding the face image to face recognition systems. In this chapter, we introduce a novel two-stream CNN-based approach for the presentation attack detection, by extracting the patch-based features and holistic depth maps from the face images. We also introduce a two-stream CNN v2 with model optimization, compression and a strategy of continuous updating. The CNN v2 shows great performances of both generalization and efficiency. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, replay attack, OULU-NPU, and SiW), with comparison to the state of the art.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Yousef Atoum & Xiaoming Liu

Authors
  1. Yaojie Liu
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  2. Joel Stehouwer
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  3. Amin Jourabloo
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  4. Yousef Atoum
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  5. Xiaoming Liu
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Corresponding author

Correspondence to Xiaoming Liu .

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Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, USA

    Ajita Rattani

  2. Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO, USA

    Reza Derakhshani

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Arun Ross

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Liu, Y., Stehouwer, J., Jourabloo, A., Atoum, Y., Liu, X. (2019). Presentation Attack Detection for Face in Mobile Phones. In: Rattani, A., Derakhshani, R., Ross, A. (eds) Selfie Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-26972-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-26972-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26971-5

  • Online ISBN: 978-3-030-26972-2

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