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  4. PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units
 
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2020
Journal Article
Titel

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

Abstract
Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.
Author(s)
Terhörst, Philipp
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Riehl, Kevin
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Rot, Peter
Univ. of Ljubljana
Bortolato, Blaz
Univ. of Ljubljana
Kirchbuchner, Florian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Struc, Vitomir
Univ. of Ljubljana
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Zeitschrift
IEEE access
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
DOI
10.1109/ACCESS.2020.2994960
File(s)
N-589852.pdf (2.72 MB)
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Digitized...

  • Lead Topic: Smart Cit...

  • Research Line: Comput...

  • Research Line: Human ...

  • biometrics

  • privacy enhancing tec...

  • privacy protection

  • face recognition

  • ATHENE

  • CRISP

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