Exploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognition

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

Abstract

Finger vein patterns are a promising biometric trait because of their higher privacy and security features compared to face and finger prints. Finger vein recognition methods have been researched extensively, especially deep learning based methods such as Convolutional Neural Networks. These methods show promising recognition performance, but their low degree of generalization and adaptability results in much lower and inconsistent recognition performance in cross database scenarios. Despite these drawbacks, much less research has gone into the generalization and adaptability of these deep learning methods.

This study addresses these issues and proposes an unsupervised learning approach, namely a patch-based Convolutional Auto-encoder for learning finger vein representations. Our proposed approach outperforms traditional baseline finger recognition methods on the UTFVP, SDUMLA-HMT, and PKU datasets, and achieves state-of-the-art performance on the UTFVP dataset with 0.24\% EER. It also indicates a noticeably higher generalization of finger vein features across different datasets compared to a supervised method. The findings of this work offer promising advancements in achieving robust finger vein recognition in real-life scenarios, due to the enhanced generalization and adaptability of our proposed method.
Original languageEnglish
Title of host publicationBIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group
EditorsNaser Damer, Marta Gomez-Barrero, Kiran Raja, Christian Rathgeb, Ana F. Sequeira, Massimiliano Todisco, Andreas Uhl
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)979-8-3503-3655-9
ISBN (Print)979-8-3503-3656-6
DOIs
Publication statusPublished - 2023
Event22nd International Conference of the Biometrics Special Interest Group, BIOSIG 2023 - Darmstadt, Germany
Duration: 20 Sept 202322 Sept 2023
Conference number: 22

Publication series

NameProceedings International Conference of the Biometrics Special Interest Group (BIOSIG)
PublisherIEEE
Number22
Volume2023
ISSN (Electronic)1617-5468

Conference

Conference22nd International Conference of the Biometrics Special Interest Group, BIOSIG 2023
Abbreviated titleBIOSIG
Country/TerritoryGermany
CityDarmstadt
Period20/09/2322/09/23

Keywords

  • Auto-encoders
  • Cross-database
  • Finger vein recognition
  • Unsupervised learning (UL)

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