Image Processing Reference
Mapping the data into another space nonlinearly makes this method (KPCA) nonlinear,
which has been stated more superior than the linear PCA in many applications and literature.
However, it can lead to the problem of complexity as the dimension of feature space is as high
as the number of input data specially when analyzing a huge amount of data. It can also make
it too complex to find the optimized subspace of the feature space for projection.
5 Experimental results
In this section, the experiments are conducted to corroborate the performance of Gaussian
KPCA over other kinds of KPCA such as polynomial, exponential, and Laplacian PCA in
terms of finger vein recognition. Finger vein database used in the experiments consists of 500
images from 50 individuals; 10 samples from each subject were taken. In this experiment, 4,
5, 6, and 7 images are used to train and the remaining 6, 5, 4, and 3 images are used to test,
respectively. In each experiment, the accuracy is calculated using the first 100 components of
the extracted features meaning that each experiment is repeated 100 times using the first 100
features to project the data onto, and also the dimension is reduced from 60% to 85% in dif-
functions to map the data first and then applying PCA on the mapped data (KPCA) results in
acceptable accuracies varying from over 70% up to near 100% in different experiments. Poly-
nomial KPCA, however, seems to be the worst among all types of KPCA and there is a great
discrepancy between polynomial and other kinds of kernel KPCA in terms of final outputs of
the system. The results show that polynomial kernel reaches its optimized point when using
even less than ten components and it remains the same no mater how many more compon-
ents to be used. It could be considered an advantage as using this kernel can be faster than
others as it gets to its peak in the point 10 or less than that. The accuracy in polynomial KPCA,
however, is not satisfying at all and is less than the others in almost all experiments. From
another point of view, when four images are used to train, the highest accuracy obtained is
around 95%, while the accuracy rate almost reaches 99% when using seven images to train
which means, the more the number of training images is, the higher accuracy gets.
FIGURE 2 Comparison of accuracies obtained using four images to train and six to test.
FIGURE 3 Comparison of accuracies obtained using five images to train and five to test.