Two-Dimensional Sine Filter Fingerprint Enhancement Algorithm Based on Block Level

Abstract:

Enhancing low-quality fingerprint image is the effective method for improving the accuracy of minutia extraction and performance of the automatic fingerprint identification system. Fingerprint image is one kind of regular texture image in nature. To design two-dimensional sinusoidal surface model which accorded with the ridge gray distribution rule and propose the fingerprint image enhancement algorithm based on the two-dimensional sinusoidal surface model after analyzing the basic character of the fingerprint image. The experimental results indicate that the fingerprint image enhancement algorithm has better connecting ability for the broken ridges than the Gabor fingerprint enhancement algorithm. The algorithm can improve effectively the fingerprint image enhancement result and the accuracy of the minutiae extraction.

• Periodical: Applied Mechanics and Materials (Volumes 88-89)
• Pages: 596-603
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.88-89.596
• Online since: August 2011
• Authors: Xiao Si Zhan, Ya Yun Chu
• Keywords: Fingerprint Enhancement, Fingerprint Identification, Gabor Filter, Sine Filter

References

[1] Luo Xiping, Tian Jie. Image Enhancement and Minutia Matching Algorithm in Automatic Fingerprint Identification System. Journal of Software, 2002, 13(5): 946 – 956.

[2] B. G. Sherlock, D. M. Monro and K. Millard. Fingerprint enhancement by directional fourier filter. IEE Proc. Vis. Image Signal Processing, 1994, 141(2): 87 – 94.

DOI: 10.1049/ip-vis:19949924

[3] L. Hong, Y. Wan and A. K. Jain. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on PAMI, 1998, 20(8): 777 – 789.

DOI: 10.1109/34.709565

[4] Y. Yin, X. Zhan, T. Tan, et al. An Algorithm Based on Gabor Function for Fingerprint Enhancement and Its Application. Journal of Software. 2003, 14(3): 483 – 489.

[5] W. Wang, J. Li, F. Huang, et al. Fingerprint Enhancement based on Log-Gabor. Computer Science, 2007, 34(7): 241 – 243.

[6] L. Hong, A. K. Jain, S. Pankanti et al. Fingerprint enhancement. Proceedings of IEEE Workshop on Applications of Computer Vision, Sarasota, FI, (1996): 202 – 207.

[7] T. Kamei and M. Mizoguchi. Image filter designfor fingerprint enhancement. Proc. ISVC'95, Coral Gables, FL, (1995): 109 – 114.

[8] Y. Wu, L. Yang. An Improved Orientation Filter-based Algorithm for Fingerprint Enhancement. Jourmal of Huazhong University Science & Technology ( Nature Science Edition ), 2007, 35(2): 22 – 25.

[9] D. C. Douglas Hung. Enhancement feature purification of fingerprint images. Pattern Recognition, 1993, 26(11): 1661 – 1671.

DOI: 10.1016/0031-3203(93)90021-n

[10] L. O'Gorman and J. V. Neckerson. An approach to fingerprint filter design. Pattern Recognition, 1998, 22(1): 29 – 38.

[11] J. Cheng, J. Tian, Y. He. Fingerprint Enahcement Based on Nonlinear Diffusion Filter. Journal of ACTA Automatic Sinica , 2004, 20(6): 854 – 862.

[12] Z. Peng, X. Peng, G. Wu. Fingerprint Enhancement Algorithm Based on Non-stationary Signal Frequency Spectrum Analysis. Journal of Data Acquisition and Processing, 2008, 23(1): 35 – 39.

[13] X. Zhan, Y. Yin, X. Meng, et al. Research on Fingerprint Image Enhancement Algorithm Based on Two-Dimensional Sine Quadric Surface Filter. Journal of Shandong University ( Engneering Science Edition), 2009, 39(2): 8 – 14.

[14] X. Zhan, X. Ning, Y. Yin, et al. The Algorithm for Distilling Fingerprint Orientation in Multi-level Block Size. Journal of Nanjing University (Nature Science Edition), 2003, 39(4): 476~482.

[15] Y. Chen, Y. Yin, X. Zhang, et al. A Method Based on Statistical Window for Ridge Distance Estimation in Fingerprint Image. Journal of Image and Graphics, 2003, 8(3): 266~270.