Palmprint based Cancelable Biometric Authentication System
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:51789", author = "Ying-Han Pang and Andrew Teoh Beng Jin and David Ngo Chek Ling", title = "Palmprint based Cancelable Biometric Authentication System", 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.", volume = "1", number = "9", pages = "2671-7", }