RT Journal Article
A1 Raid R.O. Al-Nima
A1 Musab T.S. Al-Kaltakchi
A1 Saadoon A.M. Al-Sumaidaee
A1 Satnam S. Dlay
A1 Wai Lok Woo
A1 Tingting Han
A1 Jonathon A. Chambers

PB iet
T1 Personal verification based on multi-spectral finger texture lighting images
JN IET Signal Processing
VO 12
IS 9
SP 1154
OP 1164
AB 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.
K1 wavelength 460 nm
K1 effective classifier
K1 blue light
K1 feature extraction
K1 spectral lighting sensors
K1 standard PNN
K1 finger texture images
K1 personal verification
K1 white light
K1 RPNN
K1 single texture descriptor
K1 recognition performance
K1 recognition model
K1 multispectral FT lighting images
K1 multispectral CASIA database
K1 equal error rates
K1 multispectral illuminations
K1 surrounded patterns code
K1 re-enforced probabilistic neural network
K1 cost reduction
DO https://doi.org/10.1049/iet-spr.2018.5091
UL https://digital-library.theiet.org/;jsessionid=2p0asj9gv60eu.x-iet-live-01content/journals/10.1049/iet-spr.2018.5091
LA English
SN 1751-9675
YR 2018
OL EN