Browse > Article
http://dx.doi.org/10.5351/KJAS.2012.25.6.1027

Statistical Fingerprint Recognition Matching Method with an Optimal Threshold and Confidence Interval  

Hong, C.S. (Department of Statistics, Sungkyunkwan University)
Kim, C.H. (Research Institute of Applied Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.6, 2012 , pp. 1027-1036 More about this Journal
Abstract
Among various biometrics recognition systems, statistical fingerprint recognition matching methods are considered using minutiae on fingerprints. We define similarity distance measures based on the coordinate and angle of the minutiae, and suggest a fingerprint recognition model following statistical distributions. We could obtain confidence intervals of similarity distance for the same and different persons, and optimal thresholds to minimize two kinds of error rates for distance distributions. It is found that the two confidence intervals of the same and different persons are not overlapped and that the optimal threshold locates between two confidence intervals. Hence an alternative statistical matching method can be suggested by using nonoverlapped confidence intervals and optimal thresholds obtained from the distributions of similarity distances.
Keywords
AUC; credit evaluation; cut-off; distance; FAR; FRR; minutiae; similarity; ROC;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Cappelli, R., Maltoni, D., Wayman, J. L. and Jain, A. K. (2006). Performance evaluation of fingerprint verification system, IEEE Trans. Pattern Analysis and Machine Intelligence, 28, 3-18.   DOI   ScienceOn
2 Connell, F. A. and Koepsell, T. D. (1985). Measures of gain in certainty from a diagnostic test, American Journal of Epidemiology, 121, 744-753.   DOI   ScienceOn
3 Hong, C. S. and Joo, J. S. (2010). Optimal thresholds from non-normal mixture, The Korean Journal of Applied Statistics, 23, 943-953.   과학기술학회마을   DOI   ScienceOn
4 Hong, C. S., Kim, G. C. and Jeong, J. A. (2012). Bivariate ROC curve, The Korean Journal of Applied Statistics, 19, 277-286.   과학기술학회마을   DOI   ScienceOn
5 Jain, A. K., Prabhakar, S., Hong, L. and Pankanti, S. (2000). Filterbank-based fingerprint matching, IEEE Trans. Image Processing, 9, 846-859.   DOI   ScienceOn
6 Jain, A. K., Ross, A. and Prabhakar, S. (2001). Fingerprint matching using minutiae and texture features, Appeared in Proc. of Int'l Conference on Image Processing(ICIP), 3, 282-285.
7 Kim, S. H. and Choi, T. Y. (2004). A fingerprint matching algorithm based on the Voronoi diagram, The Institute of Electronics Engineers, 41, 247-252.
8 Krzanowski, W. J. and Hand, D. J. (2009). ROC Curves for Continuous Data, Chapman & Hall/CRC, Boca Raton, Florida. Has Been Selected, Clinical Chemistry, 32, 1341-1346.
9 Lambert, J. and Lipkovich, I. (2008). A macro for getting more out of your ROC curve, SAS Global Forum, 231.
10 Maio, D. and Maltoni, D (1997). Direct gray-scale minutiae detection in fingerprints, IEEE Transactions on Pattern Analysis Machine Intelligence, 19, 27-40.   DOI   ScienceOn
11 Moses, L. E., Shapiro, D. and Littenberg, B. (1993). Combining independent studies of a diagnostic test into a summary ROC curve: Data-analytic approaches and some additional considerations, Statistics in Medicine, 12, 1293-1316.   DOI   ScienceOn
12 Na, H. J. and Kim, C. S. (2004). The study on the extraction of the minutiae and singular point for fingerprint matching, Proceedings of the Korea Multimedia Society Conference.
13 Park, J. J. and Lee, K. H. (2005). Fingerprint recognition information of ridge shape of minutiae, The Korea Institute of Signal Processing and Systems, 2, 67-73.   과학기술학회마을
14 Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction, University Press, Oxford.
15 Perkins, N. J. and Schisterman, E. F. (2006). The inconsistency of "Optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve, American Journal of Epidemiology, 163, 670-675.   DOI   ScienceOn
16 Yang, S. R., Kim, S. M. and Cho, S. B. (2004). A grid-based matching algorithm for high performance fingerprint recognition, Korean Institute of Information Scientists and Engineers, 31, 727-729.
17 Tong, X., Huang, J., Tang, X. and Shi, D. (2005). Fingerprint minutiae matching using the adjacent feature vector, Pattern Recognition Letters, 26, 1337-1345.   DOI   ScienceOn
18 Tsutomu, M., Hiroyuki, M., Koji, Y. and Satoshi, H. (2002). Impact of artificial gummy fingers on fingerprint system, Optical Security and Counterfeit Deterrence Techniques IV, 467, 275-289.
19 Ward, C. D. (1986). The Differential Positive Rate, a Derivative of Receiver Operating Characteristic Curves Useful in Comparing Tests and Determining Decision Levels, Clinical Chemistry, 32, 1428- 1429.
20 Youden, W. J. (1950). Index for rating diagnostic test, Cancer, 3, 32-35.   DOI