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Fingerprint Image Quality and Prediction of Matching Performance
Abstract
Due to their high reliability, fingerprints have been extensively used as a biometric identifier. The performance of an automatic fingerprint authentication system relies heavily on the fingerprint image quality as seen in several studies. In this work, we present a new method to quantify fingerprint image quality which is relevant to matcher performance. The ultimate goal of this research is to determine and overcome the underlying causes of the poor match. Newly developed wavelet features and previously developed spatial features, as inputs to a fuzzy c-means classifier, are used predict matcher performance. Results are obtained for two different matchers, namely NFIS bozorth3 and Verifinger (Neurotechnologija), for two different optical sensors,Crossmatch Verifier 300 and Secugen Hamster III.
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References
J. D.Woodward, N. M. Orlans, and P. T. Higgins, Biometrics. McGraw-Hill/Osborne, 2003.
A. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society. Kluwer Academic Publisher, 1999.
D. Maltonie, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. Springer-Verlag New York, Inc., 2003.
R.K.Rowe, “Multispectral fingerprint imaging,” Workshop on Biometrics and E-Authentication over open networks, Gaithesburg, 2005.
L. Hong, Y. Wan, and A. Jain, “Fingerprint image enhancement:Algorithm and performance evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777–789, 1998.
A. Jain, L. Hong, and R. Bolle, “On-line fingeroint verification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19,no. 4, pp. 302–314, 1997.
S. Schuckers, “Spoofing and anti-spoofing measures,” in Information Security Technical Report, vol. 7, 2002, pp. 56–62.
V. Valencia and C. Horn, “Biometric liveness testing,” in Biometrics.McGraw-Hill/Osborne, 2003.
L. Thalheim and J. Krissler, “Body check: biometric access protection devices and their programs put to the test,” c’t magzine, Nov 2002.
C. Wilson, R. A. Hicklin, M. Bone, H. Korves, P. Grother, B. Ulery,R. Micheals, M. Zoepfl, S. Otto, and C. Watson, “Fingerprint vendor technology evaluation 2003: Summary of results and analysis report,”NIST, Tech. Rep., 2003.
C. Watson. (1998) NIST Special Database 24: Live-Scan Digital Video Fingerprint Database. Natl. Institutes of Health, Gaithersburg, Md. http://www.nist.gov/srd/nistsd24.htm. [Online].Available: http://www.nist.gov/srd/nistsd24.htm
N. B. Nill and B. H. Bouzas, “Objective image quality measure derived from image power spectra,” Optic Engineering, vol. 31, no. 4, pp. 813–825, 1992.
B. R., P. S., and Y. Yi-Sheng., “System and method for determining the quality of fingerprint images,” U.S. Patent No. 5963656, Oct 1999.
N. Ratha, “Enhancing security and privacy in biometrics-based authentication systems,” IBM systems journal, vol. 40, pp. 614–6134, 2001.
“Performance of biometric quality measures,” IEEE Trans. Pattern Anal.Mach. Intell., vol. 29, no. 4, pp. 531–543, 2007, member-Grother, Patrick and Member-Tabassi, Elham.
J. Qi, D. Abdurrachim, D. Li, and H. Kunieda, “A hybrid method for fingerprint image quality calculation,” Automatic Identification Advanced Technologies, IEEE Workshop on, vol. 0, pp. 124–129, 2005.
J. L. Fleiss, “Measuring nominal scale agreement among many raters,”Psychological Bulletin, vol. 76, no. 5, pp. 378–382, 1971.
T. E., W. C., and W. C, “Fingerprint image quality,” NIST Research Report NISTR, vol. 7151, August 2004.
D. M and V. M., “Wavelet based texture retrieval using generalized gaussian density and kullback-leibler distance,” IEEE TIP, vol. 11, no. 2,pp. 146–158, Feb 2002.
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