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

Authors: Amir Hajian, Sepehr Damavandinejadmonfared

Abstract:

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.

Keywords: Biometrics, finger vein recognition, Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096329

References:

[1] A. K. Jain, A. Ross, and S. Prabhakar, "An Introduction to Biometric Recognition,” IEEE Trans. Circuits Syst., vol. 14, no. 1, pp. 4–20, 2004.
[2] B. Schouten and B. Jacobs, "Biometrics and their use in e-passports,” Image Vis. Comput., vol. 27, no. 3, pp. 305–312, Feb. 2009.
[3] B. A. Rosdi, C. W. Shing, and S. A. Suandi, "Finger vein recognition using local line binary pattern.,” Sensors (Basel)., vol. 11, no. 12, pp. 11357–71, Jan. 2011.
[4] M. Himaga and K. Kou, "Finger Vein Authentication Technology,” System.
[5] P.-Y. Yin, Pattern Recognition Techniques, Technology and Applications, no. November 2008. 2008, p. 626.
[6] C. Wen and J. Zhang, "Palmprint Recognition based on Gabor Wavelets and 2-Dimensional PCA&PCA,” in Internation Conference on Wavelet Analysis and Patteren Recognition, 2007, pp. 2–4.
[7] C. Vision, "A Comparison of Face Recognition Methods Final Project Report A Combined Project for,” Artif. Intell., 2003.
[8] W. Zuo, D. Zhang, and K. Wang, "Bidirectional PCA with assembled matrix distance metric for image recognition.,” IEEE Trans. Syst. Man. Cybern. B. Cybern., vol. 36, no. 4, pp. 863–72, Aug. 2006.
[9] D. Zhang and Z.-H. Zhou, ": Two-directional two-dimensional PCA for efficient face representation and recognition,” Neurocomputing, vol. 69, no. 1–3, pp. 224–231, Dec. 2005.
[10] C. Yu, H. Qing, and L. Zhang, "K2DPCA Plus 2DPCA: An Efficient Approach for Appearance Based Object Recognition,” 2009 3rd Int. Conf. Bioinforma. Biomed. Eng., pp. 1–4, Jun. 2009.
[11] J. Yang, D. Zhang, A. F. Frangi, and J. Yang, "Two-dimensional PCA: a new approach to appearance-based face representation and recognition.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 1, pp. 131–7, Jan. 2004.
[12] V. D. M. Nhat and S. Lee, "Kernel-based 2DPCA for Face Recognition,” 2007 IEEE Int. Symp. Signal Process. Inf. Technol., pp. 35–39, Dec. 2007.
[13] K. I. Kim, K. Jung, and H. J. Kim, "Face Recognition using Kernel Principal Component Analysis,” Signal Processing, vol. 9, no. 2, pp. 40– 42, 2002.
[14] R. Jenssen, "Kernel entropy component analysis.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 847–60, May 2010.
[15] B. H. Shekar, M. Sharmila Kumari, L. M. Mestetskiy, and N. F. Dyshkant, "Face recognition using kernel entropy component analysis,” Neurocomputing, vol. 74, no. 6, pp. 1053–1057, Feb. 2011.