Hand vein-based biometric authentication with convolutional neural networks and support vector machines

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 DOI: doi.org Paper Download Link (Source) paperpublications.org International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE), ISSN 2349-7815, Paper Publications, Website: www.paperpublications.org {"references": ["[1] \tN. A. Al-johania and L. A. Elrefaei, \"Dorsal hand vein recognition by convolutional neural networks: Feature learning and transfer learning approaches,\" International Journal of Intelligent Engineering and Systems, vol. 12, pp. 178\u2013191, 2019.", "[2] \tP. Arora, S. Srivastava, M. Hanmandlu, and S. Bhargava, \"Robust authentication using dorsal hand vein images,\" IEEE Intelligent Systems,, vol. 34, pp. 25\u201335, 2019.", "[3] \tBeukes, \"Hand vein-based biometric authentication with limited training samples,\" Master's thesis, Stellenbosch University,, 2018.", "[4] \tE. Beukes and J. Coetzer, \"Hand vein-based biometric authentication using two-channel similarity measure networks,\" in 2020 International SAUPEC/RobMech/PRASA Conference, University of Cape Town, Cape Town, South Africa, jan 2020, pp. 1\u20136.", "[5] \tGarbin, X. Zhu, and O. Marques, \"Dropout vs. batch normalization: an empirical study of their impact to deep learning,\" Multimedia Tools and Applications,, vol. 79, p. 12777\u201312815, 2020.", "[6] \tI. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, deeplearningbook.org.", "[7] \tS. Gupta and Y. Kaur, \"Review of different local and global contrast enhancement techniques for a digital image,\" International Journal of Computer Applications,, vol. 100, pp. 18\u201323, 2014.", "[8] \tH. Hasan, H. Z. M. Shafri, and M. Habshi, \"A comparison between support vector machine (SVM) and convolu- tional neural network (CNN) models for hyperspectral image classification,\" in IOP Conference Series: Earth and Environmental Science, Volume 357, Sustainable Civil and Construction Engineering Conference,, Univer- sity Putra Malaysia, Kuala Lumpur, Malaysia, aug 2019.", "[9] \tN. V. Krishnaveni, K. Sivasankari, and V. Vijayan, \"Personal authentication using hand vein,\" International Journal of Engineering Research and Technology (IJERT),, vol. 3, pp. 2333\u20132339, 2014. [Online]. Available: ijert.org using-hand-vein", "[10] \tM. I. Obayya, M. El-Ghandour, and F. Alrowais, \"Contact- less palm vein authentication using deep learning with bayesian optimization,\" IEEE Access,, vol. 9, pp. 1940\u2013 1957, 2021.", "[11] \tL. Prechelt, \"Automatic early stopping using cross valida- tion: quantifying the criteria,\" Neural Networks, vol. 11, pp. 761\u2013767, 1998.", "[12] \tS. Processing. (2022) Credit card fraud statistics. [Online]. Available: shiftprocessing.com statistics/", "[13] \tB. Sankur. (2011) Bosphorus hand vein database. Bogazici University. [Online]. Available: bosphorus.ee.boun.edu.tr", "[14] \tG. Sathish, S. Narmadha, S. Saravanan, and S. U. Mah- eswari, \"Personal authentication system using hand vein biometric,\" International journal of computer technology and applications,, vol. 3, pp. 383\u2013391, 2012.", "[15] \tShorten and T. M. Khoshgoftaar, \"A survey on image data augmentation for deep learning,\" Journal of Big Data,, vol. 6, pp. 1\u201348, 2019."]}