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Effective finger vein-based
Kernel principal component analysis
S. Damavandinejadmonfared; V. Varadharajan Department of Computing, Advanced Cyber Security Research Centre, Macquarie
University, Sydney, New South Wales, Australia
Kernel functions have been very useful in data classification for the purpose of identification and veriic-
ation so far. Applying such mappings first and using some methods on the mapped data such as princip-
al component analysis (PCA) has been proven novel in many different areas. A lot of improvements have
been proposed on PCA, such as kernel PCA, and kernel entropy component analysis, which are known
as very novel and reliable methods in face recognition and data classification. In this paper, we imple-
mented four different kernel mapping functions on finger database to determine the most appropriate
one in terms of analyzing finger vein data using one-dimensional PCA. Extensive experiments have been
conducted for this purpose using polynomial, Gaussian, exponential, and Laplacian PCA in four difer-
ent examinations to determine the most significant one.
Finger vein recognition
Principal component analysis (PCA)
Kernel principal component analysis (KPCA)
1 Introduction
The importance of reliability in veriication and identiication has gained lots of atention re-
cently [ 1 ]. Finger vein is a newly proposed method of biometrics that has been able to gain
many researchers' atention due to the fact that it is something internal and reliable to be used
for this purpose. Furthermore, it has been proven by the medical studies that finger vein pat-
tern is unique and stable [ 2 ]. As the data in finger vein recognition [ 3 - 5 ] is “image,” some face
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