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Different Dimension Issues in Deep Feature Space for Finger-Vein Recognition

  • Yiqi Zhong
  • , Jiahui Li
  • , Tingting Chai*
  • , Shitala Prasad
  • , Zhaoxin Zhang
  • *Corresponding author for this work
  • Faculty of Computing, Harbin Institute of Technology
  • Agency for Science, Technology and Research, Singapore

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

Abstract

Hand-crafted approaches were the dominating solutions and recently, more convolutional neural network (CNN)-based methods have been proposed for finger-vein recognition. However, the previous deep learning methods usually designed the network architecture with increasing layers and parameters, which incurs device memory issues and processing speed issues. Although many researchers have devoted to design image enhancement algorithms to improve the recognition performance of hand-crafted methods, it is interesting to investigate whether deep learning method can achieve satisfactory performance without image enhancement. This paper focuses on two different dimension issues: lightweight CNN design and the impact of image enhancement on deep learning methods. The experimental results demonstrate that the proposed method LFVRN is comparable or superior to the prior competition winners. In addition, image enhancement is validated not inevitable for the proposed lightweight CNN model LFVRN.

Original languageEnglish
Title of host publicationBiometric Recognition - 15th Chinese Conference, CCBR 2021, Proceedings
EditorsJianjiang Feng, Junping Zhang, Manhua Liu, Yuchun Fang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages295-303
Number of pages9
ISBN (Print)9783030866075
DOIs
StatePublished - 2021
Externally publishedYes
Event15th Chinese Conference on Biometric Recognition, CCBR 2021 - Shanghai, China
Duration: 10 Sep 202112 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12878 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Chinese Conference on Biometric Recognition, CCBR 2021
Country/TerritoryChina
CityShanghai
Period10/09/2112/09/21

Keywords

  • CNN-based method
  • Finger-vein recognition
  • Image enhancement
  • Network architecture design

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