Aliveness Detection of Fingerprints using Multiple Static Features
Fake finger submission attack is a major problem in fingerprint recognition systems. In this paper, we introduce an aliveness detection method based on multiple static features, which derived from a single fingerprint image. The static features are comprised of individual pore spacing, residual noise and several first order statistics. Specifically, correlation filter is adopted to address individual pore spacing. The multiple static features are useful to reflect the physiological and statistical characteristics of live and fake fingerprint. The classification can be made by calculating the liveness scores from each feature and fusing the scores through a classifier. In our dataset, we compare nine classifiers and the best classification rate at 85% is attained by using a Reduced Multivariate Polynomial classifier. Our approach is faster and more convenient for aliveness check for field applications.
[1] S. Prabhakar, and A. Jain, "Decision-level Fusion in Fingerprint
Verification," Pattern Recognition, vol. 35, no. 4, 2002, pp. 861-874.
[2] N.K. Ratha, J.H. Connell, and R.M. Bolle, "An analysis of minutiae
matching strength," Proc. AVBPA 2001, Third International Conference
on Audio- and Video-Based Biometric Person Authentication, pp.136,
August. 1999.
[3] T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, "Impact of
Artificial Gummy Fingers on Fingerprint Systems," Proc. of SPIE,
Optical Security and Counterfeit Deterrence techniques IV, vol.4677, pp.
275-289, 2002.
[4] M. Sandstrom, "Liveness Detection in Fingerprint Recognition Systems,"
Master-s Thesis, Linkoping University, Linkoping, Sweden, June 2004.
[5] R. Derakhshani, S.A.C. Schuckers, L.A. Hornak, and L.O. Gorman,
"Determination of vitality from a non-invasive biomedical measurement
for use in fingerprint scanners," Pattern Recognition, vol. 36, pp.
383-396, 2003.
[6] A. Antonelli, R. Cappelli, D. Maio, and D. Maltoni, "Fake Finger
Detection by Skin Distortion Analysis," IEEE Trans. Information
Forensics and Security, vol. 1, no.3, pp. 360-373, September 2006.
[7] Y. S. Moon, J. S. Chen, K. C. Chan, K. So. And K. C. Woo, "Wavelet
based fingerprint liveness detection," Electron. Lett., vol. 41, no. 20, pp.
1112-1113, 2005.
[8] A. Abhyankar, and S. Schuckers, "Fingerprint liveness detection using
local ridge frequencies and multiresolution texture analysis techniques,"
2006 IEEE International Conference on Image Processing, pp. 321-324,
October, 2006.
[9] A.R. Roddy, and J.D. Stosz, "Fingerprint Features - Statistical Analysis
and System Performance Estimates," Proceedings of the IEEE, vol. 85,
no. 9, pp. 1390-1421, 1997.
[10] A.R. Roddy, and J.D. Stosz, "Fingerprint Feature Processing Techniques
and Poroscopy," Intelligent Biometric Techniques in Fingerprint and
Face Recognition, CRC Press, Boca Raton, 1999.
[11] Bindra, B, Jasuja O.P, and Singla A.K, "Poroscopy: A method of personal
identification revisited" Anil Aggrawal's Internet Journal of Forensic
Medicine and Toxicology, vol. 1, no. 1, 2000.
[12] Viencent Levesque, "Measurement of Skin Deformation Using
Fingerprint Feature Tracking," Master-s Thesis, McGill University,
Montreal, Canada, November 2002.
[13] Ph. Refregier, "Optimal trade-off filters for noise robustness, sharpness of
the correlation peak, and Horner efficiency," Opt. Lett., vol.16, pp.
829-831, 1991.
[14] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of
Fingerprint Recognition. New York: Springer, 2003.
[15] C.F. Hester, and D. Casasent, "Multivariant technique for multiclass
pattern recognition," Appl. Opt. vol. 19, pp. 1758-1761, 1980.
[16] P. Hennings, J. Thornton, J. Kovacevic, and B.V.K. Vijaya Kumar,
"Wavelet packet correlation methods in biometrics," Appl. Opt. vol. 44,
no. 5, 2005.
[17] K. -A. Toh, Q. -L. Tran, and D. Srinivasan, "Benchmarking a Reduced
Multivariate Polynomial Pattern Classifier," IEEE Trans. Pattern
Analysis and Machine Intelligence, vol. 26, no. 6, June, 2004.
[18] Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification
2nd edition, Wiley-interscience Publication 2001.
[1] S. Prabhakar, and A. Jain, "Decision-level Fusion in Fingerprint
Verification," Pattern Recognition, vol. 35, no. 4, 2002, pp. 861-874.
[2] N.K. Ratha, J.H. Connell, and R.M. Bolle, "An analysis of minutiae
matching strength," Proc. AVBPA 2001, Third International Conference
on Audio- and Video-Based Biometric Person Authentication, pp.136,
August. 1999.
[3] T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, "Impact of
Artificial Gummy Fingers on Fingerprint Systems," Proc. of SPIE,
Optical Security and Counterfeit Deterrence techniques IV, vol.4677, pp.
275-289, 2002.
[4] M. Sandstrom, "Liveness Detection in Fingerprint Recognition Systems,"
Master-s Thesis, Linkoping University, Linkoping, Sweden, June 2004.
[5] R. Derakhshani, S.A.C. Schuckers, L.A. Hornak, and L.O. Gorman,
"Determination of vitality from a non-invasive biomedical measurement
for use in fingerprint scanners," Pattern Recognition, vol. 36, pp.
383-396, 2003.
[6] A. Antonelli, R. Cappelli, D. Maio, and D. Maltoni, "Fake Finger
Detection by Skin Distortion Analysis," IEEE Trans. Information
Forensics and Security, vol. 1, no.3, pp. 360-373, September 2006.
[7] Y. S. Moon, J. S. Chen, K. C. Chan, K. So. And K. C. Woo, "Wavelet
based fingerprint liveness detection," Electron. Lett., vol. 41, no. 20, pp.
1112-1113, 2005.
[8] A. Abhyankar, and S. Schuckers, "Fingerprint liveness detection using
local ridge frequencies and multiresolution texture analysis techniques,"
2006 IEEE International Conference on Image Processing, pp. 321-324,
October, 2006.
[9] A.R. Roddy, and J.D. Stosz, "Fingerprint Features - Statistical Analysis
and System Performance Estimates," Proceedings of the IEEE, vol. 85,
no. 9, pp. 1390-1421, 1997.
[10] A.R. Roddy, and J.D. Stosz, "Fingerprint Feature Processing Techniques
and Poroscopy," Intelligent Biometric Techniques in Fingerprint and
Face Recognition, CRC Press, Boca Raton, 1999.
[11] Bindra, B, Jasuja O.P, and Singla A.K, "Poroscopy: A method of personal
identification revisited" Anil Aggrawal's Internet Journal of Forensic
Medicine and Toxicology, vol. 1, no. 1, 2000.
[12] Viencent Levesque, "Measurement of Skin Deformation Using
Fingerprint Feature Tracking," Master-s Thesis, McGill University,
Montreal, Canada, November 2002.
[13] Ph. Refregier, "Optimal trade-off filters for noise robustness, sharpness of
the correlation peak, and Horner efficiency," Opt. Lett., vol.16, pp.
829-831, 1991.
[14] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of
Fingerprint Recognition. New York: Springer, 2003.
[15] C.F. Hester, and D. Casasent, "Multivariant technique for multiclass
pattern recognition," Appl. Opt. vol. 19, pp. 1758-1761, 1980.
[16] P. Hennings, J. Thornton, J. Kovacevic, and B.V.K. Vijaya Kumar,
"Wavelet packet correlation methods in biometrics," Appl. Opt. vol. 44,
no. 5, 2005.
[17] K. -A. Toh, Q. -L. Tran, and D. Srinivasan, "Benchmarking a Reduced
Multivariate Polynomial Pattern Classifier," IEEE Trans. Pattern
Analysis and Machine Intelligence, vol. 26, no. 6, June, 2004.
[18] Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification
2nd edition, Wiley-interscience Publication 2001.
@article{"International Journal of Information, Control and Computer Sciences:53058", author = "Heeseung Choi and Raechoong Kang and Kyungtaek Choi and Jaihie Kim", title = "Aliveness Detection of Fingerprints using Multiple Static Features ", abstract = "Fake finger submission attack is a major problem in fingerprint recognition systems. In this paper, we introduce an aliveness detection method based on multiple static features, which derived from a single fingerprint image. The static features are comprised of individual pore spacing, residual noise and several first order statistics. Specifically, correlation filter is adopted to address individual pore spacing. The multiple static features are useful to reflect the physiological and statistical characteristics of live and fake fingerprint. The classification can be made by calculating the liveness scores from each feature and fusing the scores through a classifier. In our dataset, we compare nine classifiers and the best classification rate at 85% is attained by using a Reduced Multivariate Polynomial classifier. Our approach is faster and more convenient for aliveness check for field applications. ", keywords = "Aliveness detection, Fingerprint recognition,individual pore spacing, multiple static features, residual noise.", volume = "1", number = "4", pages = "912-6", }
