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FIGURE 4 Comparison of accuracies obtained using six images to train and four to test.
FIGURE 5 Comparison of accuracies obtained using seven images to train and three to test.
It is observed from the results that the accuracies achieved using Laplacian KPCA are not
very close to those of the exponential and Gaussian methods; it is also understood that al-
though the accuracies of Gaussian KPCA are close to those of exponential KPCA, the exponen-
tial method in a majority of implementations results in less accuracy compared to Gaussian.
Results indicate that in the first experiment, where four images were used to train and the
remaining six images to test, the difference between the accuracies obtained using Gaussian,
exponential, and Laplacian were not that significant as Laplacian, exponential, and Gaussian
reached 90%, 94%, and 95%, respectively. However, the more the number of training images
get, the higher accuracy Gaussian KPCA obtains and its discrepancy in accuracy becomes lar-
ger to the point that leads to the conclusion that Gaussian KPCA is the most superior kernel
mapping in finger vein recognition systems.
6 Conclusion
The performance of four different types of KPCA on finger vein recognition has been validated
in this paper. The first contribution is that KPCA might not be efficient enough in image clas-
siication and recognition as it might be sometimes too time consuming and computationally
expensive; however, KPCA can be very reliable and accurate as it is able to deal with nonlin-
ear patterns in the data. Among the examined kernel mapping in this work, it is shown that
not only is the Gaussian KPCA the most appropriate one in comparison with the other types
of KPCA (polynomial, exponential, and Laplacian), but also this method is efficient enough to
be used in finger vein recognition as for such a big data base the accuracy is very high and
[1] Jain AK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Trans
Circuits Syst. 2004;14(1):4-20.
[2] Kumar A, Zhou Y. Human identification using finger images. IEEE T Image Process.
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