Finger Vein Verification with a Convolutional Auto-encoder

Tuğçe Arican*, Raymond N.J. Veldhuis, Luuk Spreeuwers

*Corresponding author for this work

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

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Abstract

Unsupervised learning methods can learn generalized features without label in-
formation, which can be advantageous for small datasets like finger veins. This
work explores the possibility of learning finger vein representations with an un-
supervised learning method called Convolutional Auto-encoder(CAE), and pro-
posed a modification to the loss function to learn better representations of the
vein patterns. The results indicate that the CAE is powerful in reconstructing
global structures, yet failed to reconstruct vein patterns. The CAE with a Log-
likelihood ratio classifier achieved 6.74% EER on UTFVP dataset. Though the
performance of the system is far from the state-of-the-art, the findings imply that
the global finger structures contribute to the identity, and are powerful enough
to be used as an additional information source for finger vein recognition.
Original languageEnglish
Title of host publicationProceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux
Place of PublicationEindhoven, the Netherlands
PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
Pages43-51
ISBN (Electronic)978-90-386-5318-1
Publication statusPublished - 2021
Event41st Symposium on Information Theory and Signal Processing
in the Benelux 2021
- Online Symposium
Duration: 20 May 202121 May 2021
Conference number: 41

Conference

Conference41st Symposium on Information Theory and Signal Processing
in the Benelux 2021
CityOnline Symposium
Period20/05/2121/05/21

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