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  4. On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection
 
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2019
Conference Paper
Titel

On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection

Abstract
Face morphing attacks are verifiable to multiple identities, leading to faulty identity links. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work studies detecting these attacks as anomalies and discusses the performance and generalization over different morphing types. We also analyze the accuracy and generalization effect of including different amounts of attack contamination in the anomaly training data (novelty vs. outlier). This is performed with two baseline 2-class classifiers, two approaches for anomaly detection, two image feature extractions, two morphing types, and variations in contamination levels and tolerated training errors. The results points out the relative lower performance, but higher generalization ability, of anomaly detection in comparison to 2-class classifiers, along with the benefits of contaminating the anomaly training data.
Author(s)
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Grebe, Jonas Henry
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Zienert, Steffen
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kirchbuchner, Florian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019
Project(s)
CRISP
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Biometrics - Theory, Applications and Systems (BTAS) 2019
Thumbnail Image
DOI
10.1109/BTAS46853.2019.9185995
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic- Smart Cit...

  • Lead Topic- Visual Co...

  • Research Line- Comput...

  • Research Line- Human ...

  • Biometrics

  • Machine learning

  • Face recognition

  • Spoofing attacks

  • CRISP

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