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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Author 1: Roza Waleed Ali
Author 2: Junaidah Mohamed Kassim
Author 3: Siti Norul Huda Sheikh Abdullah

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0100120

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 1, 2019.

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Abstract: Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

Keywords: Finger vein recognition; SLA; ELM; SVM; HOG; straight line approximate

Roza Waleed Ali, Junaidah Mohamed Kassim and Siti Norul Huda Sheikh Abdullah, “Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(1), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100120

@article{Ali2019,
title = {Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100120},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100120},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Roza Waleed Ali and Junaidah Mohamed Kassim and Siti Norul Huda Sheikh Abdullah}
}


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