Arican, Tuğçe

Spreeuwers, Luuk

Veldhuis, Raymond N.J.

English

Unsupervised learning methods can learn generalized features without label in-formation, which can be advantageous for small datasets like finger veins. Thiswork 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 thevein patterns. The results indicate that the CAE is powerful in reconstructingglobal structures, yet failed to reconstruct vein patterns. The CAE with a Log-likelihood ratio classifier achieved 6.74% EER on UTFVP dataset. Though theperformance of the system is far from the state-of-the-art, the findings imply thatthe global finger structures contribute to the identity, and are powerful enoughto be used as an additional information source for finger vein recognition

University of Twente Research Information

Finger Vein Verification with a Convolutional Auto-encoder

https://ris.utwente.nl/ws/files/275248754/proceedings_SITB2021.pdf

Finger Vein Verification with a Convolutional Auto-encoder

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University of Twente Research Information