Personal Verification Based on Multi-Spectral Finger Texture Lighting Images.pdf (2.82 MB)
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Personal verification based on multi-spectral finger texture lighting images

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journal contribution
posted on 21.05.2019, 11:13 by RRO Al-Nima, MTS Al-Kaltakchi, SAM Al-Sumaidaee, SS Dlay, WL Woo, T Han, JA Chambers
Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re-enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi-spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.

Funding

The first, second and third authors would like to thank the Ministry of Higher Education and Scientific Research (MOHESR), Iraq. • Some authors want to give their thanks to: "RC grant EP/P015387/1". • “The Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database version 1.0”. • “IIT Delhi Palmprint Image Database version 1.0”. • “Portions of the research in this paper use the CASIA-MSPalmprintV1 collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA)”.

History

Citation

IET Signal Processing, 2018, 12(9), pp. 1154 - 1164

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

AM (Accepted Manuscript)

Published in

IET Signal Processing

Publisher

Institution of Engineering and Technology (IET)

issn

1751-9675

Acceptance date

27/07/2018

Copyright date

2018

Available date

21/05/2019

Publisher version

https://ieeexplore.ieee.org/document/8556571

Language

en

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