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German Conference on Pattern Recognition

GCPR 2018: Pattern Recognition pp 518–534Cite as

Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

Abstract

Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features.

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Acknowledgment

This work was supported by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within CRISP. Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office [25, 26].

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Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany

    Naser Damer, Viola Boller, Yaza Wainakh, Fadi Boutros, Philipp Terhörst, Andreas Braun & Arjan Kuijper

  2. Mathematical and Applied Visual Computing, TU Darmstadt, Darmstadt, Germany

    Arjan Kuijper

Authors
  1. Naser Damer
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  2. Viola Boller
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  3. Yaza Wainakh
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  4. Fadi Boutros
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  5. Philipp Terhörst
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  6. Andreas Braun
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  7. Arjan Kuijper
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Corresponding author

Correspondence to Naser Damer .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Damer, N. et al. (2019). Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

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