Palmprint based Cancelable Biometric Authentication System
Commenced in January 2007
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Edition: International
Paper Count: 32797
Palmprint based Cancelable Biometric Authentication System

Authors: Ying-Han Pang, Andrew Teoh Beng Jin, David Ngo Chek Ling

Abstract:

A cancelable palmprint authentication system proposed in this paper is specifically designed to overcome the limitations of the contemporary biometric authentication system. In this proposed system, Geometric and pseudo Zernike moments are employed as feature extractors to transform palmprint image into a lower dimensional compact feature representation. Before moment computation, wavelet transform is adopted to decompose palmprint image into lower resolution and dimensional frequency subbands. This reduces the computational load of moment calculation drastically. The generated wavelet-moment based feature representation is used to generate cancelable verification key with a set of random data. This private binary key can be canceled and replaced. Besides that, this key also possesses high data capture offset tolerance, with highly correlated bit strings for intra-class population. This property allows a clear separation of the genuine and imposter populations, as well as zero Equal Error Rate achievement, which is hardly gained in the conventional biometric based authentication system.

Keywords: Cancelable biometric authenticator, Discrete- Hashing, Moments, Palmprint.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057653

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References:


[1] R. M. Bolle, J. H. Connel, and N. K. Ratha, "Biometric perils and patches," Pattern Recognition, 35(12), pp. 2727-2738, 2002.
[2] T. B. J. Andrew, N. C. L. David, and G. Alwyn, "Biohashing: two factor authentication featuring fingerprint data and tokenised random number," Pattern Recognition, Pattern Recognition Soc., Elservier Science, to be published.
[3] S. Mallat, A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1998.
[4] R. Mukundan, and K. R. Ramakrishnan, Moment Functions in Image Analysis - Theory and Applications. World Scientific Publishing, 1998.
[5] A. Chiang, S. Liao, Q. Lu, and M. Pawlak, "Gegenbauer moment-based applications for Chinese character recognition," Proceedings of the 2002 IEEE Canadian Conference on Electrical & Computer Engineering, 2002, pp. 908-911.
[6] X. L. Simon, and Q. Lu, " A study of moment functions and its use in Chinese character recognition," Proceedings of the Fourth International Conference on Document Analysis and Recognition, vol. 2 , 1997, pp. 572 - 575.
[7] Y. H. Pang, T. B. J. Andrew, N. C. L. David, and F. S. Hiew, "Palmprint verification with moments," Journal of Computer Graphics, Visualization and Computer Vision (WSCG), ISSN 1213-6972, 12(2), 2004, pp. 325-332.
[8] C. H. Teh, and R. T. Chin, "On image analysis by the methods of moments," IEEE Trans. Pattern Analysis and Machine Intelligence, 10, 1998, pp. 496-512.
[9] A. Khotanzad, "Invariant image recognition by Zernike moments," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-12, no.5, 1990, pp. 489-497.