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  4. SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
 
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2022
Conference Paper
Title

SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data

Abstract
Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achieving state-of-the-art performance on mainstream benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2, are retracted due to credible privacy and ethical concerns. This motivates this work to propose and investigate the feasibility of using a privacy-friendly synthetically generated face dataset to train face recognition models. Towards this end, we utilize a class-conditional generative adversarial network to generate class-labeled synthetic face images, namely SFace. To address the privacy aspect of using such data to train a face recognition model, we provide extensive evaluation experiments on the identity relation between the synthetic dataset and the original authentic dataset used to train the generative model. Our reported evaluation proved that associating an identity of the authentic dataset to one with the same class label in the synthetic dataset is hardly possible. We also propose to train face recognition on our privacy-friendly dataset, SFace, using three different learning strategies, multi-class classification, label-free knowledge transfer, and combined learning of multi-class classification and knowledge transfer. The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has a high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91.87% on LFW using multi-class classification and 99.13% using the combined learning strategy. The training code and the synthetic face image dataset are publicly released 1 1 https://github.com/fdbtrs/SFace-Privacy-friendly-and-Accurate-Face-Recognition-using-Synthetic-Data.
Author(s)
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Huber, Marco  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Siebke, Patrick  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Rieber, Tim Jannik
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
IEEE International Joint Conference on Biometrics, IJCB 2022  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
Software Campus Project
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Hessisches Ministerium für Wissenschaft und Kunst
Bundesministerium für Bildung und Forschung -BMBF-  
Conference
International Joint Conference on Biometrics 2022  
Open Access
DOI
10.1109/IJCB54206.2022.10007961
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Information Technology

  • Research Line: Computer vision (CV)

  • Research Line: Machine learning (ML)

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Biometrics

  • Machine learning

  • Deep learning

  • Face recognition

  • Privacy enhancing technologies

  • ATHENE

  • CRISP

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