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  4. Learning Privacy-Enhancing Face Representations through Feature Disentanglement
 
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2020
  • Konferenzbeitrag

Titel

Learning Privacy-Enhancing Face Representations through Feature Disentanglement

Abstract
Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.
Author(s)
Bortolato, Blaz
Univ. of Ljubljana
Ivanovska, Marija
Univ. of Ljubljana
Rot, Peter
Univ. of Ljubljana
Krizaj, Janez
Univ. of Ljubljana
Terhörst, Philipp
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Peer, Peter
Univ. of Ljubljana
Struc, Vitomir
Univ. of Ljubljana
Hauptwerk
15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020. Proceedings
Konferenz
International Conference on Automatic Face and Gesture Recognition (FG) 2020
Thumbnail Image
DOI
10.1109/FG47880.2020.00007
Language
Englisch
google-scholar
IGD
Tags
  • Lead Topic: Smart Cit...

  • Lead Topic: Visual Co...

  • Research Line: Comput...

  • Research Line: Human ...

  • biometrics

  • face recognition

  • privacy enhancing tec...

  • CRISP

  • ATHENE

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