Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Converting convolutional neural networks such as MobileNets to a full integer representation is already quite a popular method to reduce the size and computational footprint of classification networks but its effect on face recognition networks is relatively unexplored. This work presents a method to reduce the size of MobileFaceNet using sub-byte quantization of the weights and activations. It was found that 8-bit and 4-bit versions of MobileFaceNet can be obtained with 98.68% and 98.63% accuracy on the LFW dataset which reduces the footprint to 25% and 12.5% of the original weights respectively. Using mixed-precision, an accuracy of 98.17% can be achieved whilst requiring only 10% of the original weight footprint. It is expected that with a larger training dataset, higher accuracies can be achieved.
Original languageEnglish
Title of host publication2022 International Conference of the Biometrics Special Interest Group (BIOSIG)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-6654-7666-9
ISBN (Print)978-1-6654-7667-6
DOIs
Publication statusPublished - 16 Sep 2022
Event21st International Conference of the Biometrics Special Interest Group, BIOSIG 2022 - Darmstadt, Germany
Duration: 14 Sep 202216 Sep 2022
Conference number: 21

Publication series

NameInternational Conference of the Biometrics Special Interest Group (BIOSIG)
PublisherIEEE
Volume2022
ISSN (Print)1617-5468

Conference

Conference21st International Conference of the Biometrics Special Interest Group, BIOSIG 2022
Abbreviated titleBIOSIG 2022
Country/TerritoryGermany
CityDarmstadt
Period14/09/2216/09/22

Keywords

  • Training
  • Quantization (signal)
  • Face recognition
  • Convolutional neural networks
  • 2023 OA procedure

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