Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis

In this paper the issue of dimensionality reduction is
investigated in finger vein recognition systems using kernel Principal
Component Analysis (KPCA). One aspect of KPCA is to find the
most appropriate kernel function on finger vein recognition as there
are several kernel functions which can be used within PCA-based
algorithms. In this paper, however, another side of PCA-based
algorithms -particularly KPCA- is investigated. The aspect of
dimension of feature vector in PCA-based algorithms is of
importance especially when it comes to the real-world applications
and usage of such algorithms. It means that a fixed dimension of
feature vector has to be set to reduce the dimension of the input and
output data and extract the features from them. Then a classifier is
performed to classify the data and make the final decision. We
analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in
this paper and investigate the optimal feature extraction dimension in
finger vein recognition using KPCA.

• Amir Hajian
• Sepehr Damavandinejadmonfared

• Biometrics
• finger vein recognition
• Principal Component Analysis (PCA)
• Kernel Principal Component Analysis (KPCA).

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