Title: GenVeins: an artificially generated hand vein database

Authors: Emile Beukes; Johannes Coetzer

Addresses: Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa ' Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa

Abstract: An artificially generated dorsal hand vein database called 'GenVeins' (see Beukes, 2023) is developed in this study for the purpose of acquiring sets of fictitious training and validation individuals which are large enough to represent the entire population. The development of said database is motivated by experimental results, which indicate that system proficiency is severely impaired when training on an insufficient number of different individuals. A number of dorsal hand vein-based authentication systems are proposed in this study for the purpose of determining whether or not the utilisation of the GenVeins database may increase system proficiency when compared to training and validating the proposed systems on small sets of different individuals. The results clearly indicate that the utilisation of the GenVeins database significantly increases system proficiency when compared to the scenario in which an insufficient number of different individuals are utilised for training and validation.

Keywords: biometric authentication; hand vein; deep learning; similarity measure networks; SMN; Siamese networks; two-channel networks; segmentation; artificial data; convolutional neural networks; CNNs.

DOI: 10.1504/IJBM.2024.141936

International Journal of Biometrics, 2024 Vol.16 No.6, pp.553 - 582

Received: 06 Sep 2023
Accepted: 01 Dec 2023
Published online: 03 Oct 2024 *

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