Abstract
This paper proposes a novel minutiae-based fingerprint matching algorithm. A fingerprint is represented by minutiae set and sampling points on all ridges. Therefore, the foreground of a fingerprint image can be accurately estimated by the sampling points. The similarity between two minutiae is measured by two parts: neighboring minutiae which are different in minutiae pattern and neighboring sampling points which are different in orientation and frequency. After alignment and minutiae pairing, Nine features are extracted to represent the matching status and penalized logistic regression (PLR) is adopted to calculate the matching score. The proposed algorithm is evaluated on fingerprint databases of FVC2002 and compared with the participants in FVC 2002. Experimental results show that the proposed algorithm achieves good performance and ranks 5th according to average equal error rate.
This paper is supported by the Project of National Natural Science Foundation of China under Grant No. 60875018 and 60621001, National High Technology Research and Development Program of China under Grant No. 2008AA01Z411, Chinese Academy of Sciences Hundred Talents Program, Beijing Natural Science Foundation under Grant No. 4091004.
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Tico, M., Kuosmanen, P.: Fingerprint Matching Using an Orientation-Based Minutia Descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1009–1014 (2003)
He, Y., Tian, J., Luo, X., Zhang, T.: Image Enhancement and Minutiae Matching in Fingerprint Verification. Pattern Recognition Letters 24(9), 1349–1360 (2003)
He, Y., Tian, J., Li, L., Chen, H., Yang, X.: Fingerprint Matching Based on Global Comprehensive Similarity. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 850–862 (2006)
Bazen, A.M., Gerez, S.H.: Fingerprint Matching by Thin-Plate Spline Modelling of Elastic Deformations. Pattern Recognition 36(8), 1859–1867 (2003)
Feng, J.: Combining Minutiae Descriptors for Fingerprint Matching. Pattern Recognition 41(1), 342–352 (2008)
Lin, H., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
Luo, X., Tian, J.: Knowledge Based Fingerprint Image Enhancement. In: Proc. 15th ICPR, pp. 4783–4786 (2000)
Graham, R.L.: An Efficient Algorithm for Determining The Convex Hull of a Finite Planar Set. Information Processing Letters 26, 132–133 (1972)
Shen, L., Tan, E.T.: Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data. IEEE/ACM Trans. On Computational Biology and Bioinformatics 2(2), 166–175 (2005)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
The 2nd fingerprint verification competition, http://bias.csr.unibo.it/fvc2002/
Wilson, C.L., Watson, C.I., Paek, E.G.: Effect of Resolution and Image Quality on Combined Optical and Neural Network Fingerprint Matching. Pattern Recognition 33(2), 317–331 (2000)
Feng, J., Ouyang, Z., Cai, A.: Fingerprint Matching Using Ridges. Pattern Recognition 39(11), 2131–2140 (2006)
Gu, J., Zhou, J., Yang, C.: Fingerprint Recognition by Combining Global Structure and Local Cues. IEEE Trans. On Image Processing 15(7), 1952–1964 (2006)
Jain, A.K., Hong, L., Bolle, R.: On-Line Fingerprint Verification. IEEE Trans.Pattern Anal. Mach. Intell. 19(4), 302–314 (1997)
Borgefors, G.: Distance Transformations in Digital Images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)
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Cao, K., Yang, X., Tian, J., Zhang, Y., Li, P., Tao, X. (2009). Fingerprint Matching Based on Neighboring Information and Penalized Logistic Regression. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_63
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DOI: https://doi.org/10.1007/978-3-642-01793-3_63
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