Vol 9 Issue 3 July 2022-September 2022
Emile Beukes, Johannes Coetzer
Abstract: The viability of utilising a convolutional neural network-based (CNN-based) feature extractor together with a support vector machine (SVM) for the purpose of identity verification by means of near infra-red (NIR) images of individuals’ dorsal hand vein patterns is investigated in this paper. More specifically, this study aims to determine whether the utilisation of an SVM, instead of a typical softmax classifier, may lead to an increase in system performance within the context of hand vein-based authentication using CNNs. The proficiency of a variety of novel hand vein-based authentication systems is first gauged by employing a softmax classifier, after which the most proficient system is selected, retrained and re-evaluated with a SVM instead of a softmax classifier. The most proficient system, in which the softmax classifier is replaced with a SVM, achieves an accuracy of 98.90% and 99.23% respectively within the context of the Bosphorus and the Wilches datasets.
Keywords: biometric authentication, hand vein, deep learning, similarity measure network, siamese networks, two- channel networks, segmentation, individual specific, convolutional neural networks, support vector machine.
Title: Hand vein-based biometric authentication with convolutional neural networks and support vector machines
Author: Emile Beukes, Johannes Coetzer
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
ISSN 2349-7815
Vol. 9, Issue 3, July 2022 - September 2022
Page No: 12-31
Paper Publications
Website: www.paperpublications.org
Published Date: 04-August-2022
