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  4. QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
 
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2022
Conference Paper
Titel

QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization

Abstract
Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrained by computational requirements is often infeasible due to the large memory required by the full-precision model. Previous compact face recognition approaches proposed to design special compact architectures and train them from scratch using real training data, which may not be available in a real-world scenario due to privacy concerns.We present in this work the QuantFace solution based on low-bit precision format model quantization. QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data. QuantFace introduces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to real training data. Through extensive evaluation experiments on seven benchmarks and four network architectures, we demonstrate that QuantFace can successfully reduce the model size up to 5x while maintaining, to a large degree, the verification performance of the full-precision model without accessing real training datasets. All training codes are publicly available.
Author(s)
Boutros, Fadi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kuijper, Arjan orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
ICPR 2022, 26th International Conference on Pattern Recognition
Project(s)
Next Generation Biometric Systems
Next Generation Biometric Systems
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Hessisches Ministerium für Wissenschaft und Kunst
Konferenz
International Conference on Pattern Recognition 2022
Thumbnail Image
DOI
10.1109/ICPR56361.2022.9955645
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Digitized...

  • Lead Topic: Smart Cit...

  • Lead Topic: Visual Co...

  • Research Line: Comput...

  • Mensch-Maschine-Inter...

  • Machine Learning (ML)...

  • Face recognition

  • Biometrics

  • Machine learning

  • Embedded systems

  • Quantization

  • ATHENE

  • CRISP

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