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  4. Lightweight Periocular Recognition through Low-bit Quantization
 
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2022
Conference Paper
Title

Lightweight Periocular Recognition through Low-bit Quantization

Abstract
Deep learning-based systems for periocular recognition make use of the high recognition performance of neural networks, which, however, is accompanied by high computational costs and memory footprints. This can lead to deployability problems, especially in mobile devices and embedded systems. Few previous works strived towards building lighter models, however, while still depending on floating-point numbers associated with higher computational cost and memory footprint. In this paper, we propose to adapt model quantization for periocular recognition. This, within the proposed scheme, leads to reducing the memory footprint of periocular recognition network by up to five folds while maintaining high recognition performance. We present a comprehensive analysis over three backbones and diverse experimental protocols to stress the consistency of our conclusions, along with a comparison with a wide set of baselines that prove the optimal trade-off between performance and model size achieved by our proposed solution. The code and pre-trained models have been made available at https://github.com/jankolf/ijcb-periocular-quantization.
Author(s)
Kolf, Jan Niklas  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kirchbuchner, Florian  orcid-logo
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  
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Hessisches Ministerium für Wissenschaft und Kunst
Conference
International Joint Conference on Biometrics 2022  
DOI
10.1109/IJCB54206.2022.10007980
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

  • Efficiency

  • ATHENE

  • CRISP

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