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  4. Compact Models for Periocular Verification Through Knowledge Distillation
 
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2020
Conference Paper
Titel

Compact Models for Periocular Verification Through Knowledge Distillation

Abstract
Despite the wide use of deep neural network for periocular verification, achieving smaller deep learning models with high performance that can be deployed on low computational powered devices remains a challenge. In term of computation cost, we present in this paper a lightweight deep learning model with only 1.1m of trainable parameters, DenseNet-20, based on DenseNet architecture. Further, we present an approach to enhance the verification performance of DenseNet-20 via knowledge distillation. With the experiments on VISPI dataset captured with two different smartphones, iPhone and Nokia, we show that introducing knowledge distillation to DenseNet-20 training phase outperforms the same model trained without knowledge distillation where the Equal Error Rate (EER) reduces from 8.36% to 4.56% EER on iPhone data, from 5.33% to 4.64% EER on Nokia data, and from 20.98% to 15.54% EER on cross-smartphone data.
Author(s)
Boutros, Fadi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Fang, Meiling
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Raja, Kiran
NTNU, Gjovik, Norway
Kirchbuchner, Florian orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kuijper, Arjan orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
BIOSIG 2020, 19th International Conference of the Biometrics Special Interest Group. Proceedings
Konferenz
Gesellschaft für Informatik, Special Interest Group on Biometrics and Electronic Signatures (BIOSIG International Conference) 2020
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Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Smart City

  • Lead Topic: Visual Computing as a Service

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • biometric

  • knowledge processing

  • deep learning

  • machine learning

  • CRISP

  • ATHENE

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