Abstract
Individuality of fingerprints can be quantified by computing the probabilistic metrics for measuring the degree of fingerprint individuality. In this paper, we present a novel individuality evaluation approach to estimate the probability of random correspondence (PRC). Three generative models are developed respectively to represent the distribution of fingerprint features: ridge flow, minutiae and minutiae together with ridge points. A mathematical model that computes the PRCs are derived based on the generative models. Three metrics are discussed in this paper: (i) PRC of two samples, (ii) PRC among a random set of n samples (nPRC) and (iii) PRC between a specific sample among n others (specific nPRC). Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the NIST4 database.
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References
United States Court of Appeals for the Third Circuit: USA v. Byron Mitchell. No. 02-2859 (2003)
Stoney, D.A.: Measurement of Fingerprint Individuality. In: Lee, H., Gaensslen, R.E. (eds.) Advances in Fingerprint Technology. CRC Press, Boca Raton (2001)
Galton, F.: Finger Prints. McMillan, London (1892)
Roxburgh, T.: Galton’s work on the evidential value of fingerprints. Indian Journal of Statistics 1, 62 (1933)
Henry, E.R.: Classification and Uses of FingerPrints, p. 54. Routledge & Sons, London (1900)
Trauring, M.: Automatic comparison of finger-ridge patterns. Nature, 197 (1963)
Pankanti, S., Prabhakar, S., Jain, A.K.: On the individuality of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8) (2002)
Dass, S.C., Zhu, Y., Jain, A.K.: Statistical Models for Assessing the Individuality of Fingerprints. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies, pp. 3–9 (2005)
Su, C., Srihari, S.N.: Generative Models for Fingerprint Individuality using Ridge Models. In: Proceedings of International Conference on Pattern Recognition. IEEE Computer Society Press, Los Alamitos (2008)
Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press, Boca Raton (1999)
Watson, C., Garris, M., Tabassi, E., Wilson, C., McCabe, R., Janet, S.: User’s Guide to NIST Fingerprint Image Software, vol. 2, pp. 80–86. NIST (2004)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Mardia, K.V., Jupp, P.E.: Directional Statistics. Wiley, Chichester (2000)
Chen, J., Moon, Y.-S.: A Minutiae-based Fingerprint Individuality Model. In: Computer Vision and Pattern Recognition, CVPR 2007 (2007)
Hsu, R.L.V., Martin, B.: An Analysis of Minutiae Neighborhood Probabilities. In: Biometrics: Theory, Applications and Systems (2008)
Srihari, S.N., Cha, S., Arora, H., Lee, S.J.: Discriminability of Fingerprints of Twins. Journal of Forensic Identification 58(1), 109–127 (2008)
Tabassi, E., Wilson, C.L., Watson, C.I.: Fingerprint Image Quality. In: NISTIR 7151, National Institute of Standards and Technology (August 2004)
Fang, G., Srihari, S.N., Srinivasan, H., Phatak, P.: Use of Ridge Points in Partial Fingerprint Matching. In: Biometric Technology for Human Identification IV, pp. 65390D1–65390D9. SPIE (2007)
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Su, C., Srihari, S.N. (2009). Probability of Random Correspondence for Fingerprints. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds) Computational Forensics. IWCF 2009. Lecture Notes in Computer Science, vol 5718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03521-0_6
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DOI: https://doi.org/10.1007/978-3-642-03521-0_6
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