Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture

Fingerprint identification is still a challenging issue for confident authentication. In this study, we present a methodology that comprises pre-processing, minutiae detection, and Gabor wavelet transform. Both Gabor wavelet and minutiae features, such as ridge bifurcation and ending enhancement, represent the significant information belonging to fingerprint images. Pre-processing algorithm affects minutiae extraction performance. So we use the dilation morphological operation and thinning for the enhancement. Then Gabor wavelet transform is applied to minutiae-extracted images to increase the identification performance. The classification problem is solved using a proper convolutional neural network (CNN) with a three-layer convolutional model and appropriate filter sizes. Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images

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1. P. Gupta, K. Tiwari and G. Arora, “Fingerprint indexing schemes: A survey,” Neurocomputing, vol. 335, pp. 352–365, 2019.

2. D. Maltoni, D. Maio, A. K. Jain and S. Prabhakar, Handbook of Fingerprint Recognition, London, England: Springer Science and Business Media, 2009.

3. S. Dargan and M. Kumar, “A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities,” Expert Syst. Appl., vol. 143, pp. 1–27, 2020.

4. F. Liu, Y. Zhao, G. Liu and L. Shen, “Fingerprint pore matching using deep features,” Pattern Recognit., vol. 102, 2020.

5. M. Puertas, D. Ramos, J. Fierrez, J. Ortega-Garcia and N. Exposito, “Towards a better understanding of the performance of latent fingerprint recognition in realistic forensic conditions,” 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp. 1638–1641.

6. D. Zhang, F. Liu, Q. Zhao, G. Lu and N. Luo, “Selecting a reference high resolution for fingerprint recognition using minutiae and pores,” IEEE Trans. Instrum. Meas., vol. 60, no. 3, pp. 863–871, 2011.

7. J. Ezeobiejesi and B. Bhanu, “Latent fingerprint image segmentation using deep neural network,” in Adv. Comput. Vis. Pattern Recognit., Berlin: Springer, pp. 83–107, 2017.

8. R. Cappelli, “Fast and accurate fingerprint indexing based on ridge orientation and frequency,” IEEE Trans. Syst. Man Cybern. B Cybern., vol. 41, no. 6, pp. 1511–1521, 2011.

9. S. O. Lee, Y. G. Kim and G. T. Park, “A feature map consisting of orientation and inter-ridge spacing for fingerprint retrieval,” in Lecture Notes in Computer Science. Berlin: Springer, pp. 184–190, 2005.

10. V. Anand and V. Kanhangad, “Pore based indexing for high-resolution fingerprints,” Proceedings of the IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), New Delhi, India, 2017, pp. 1–6.

11. K. Tiwari and P. Gupta, “Indexing fingerprint database with minutiae based coax- ial gaussian track code and quantized lookup table,” in Proceedings of the IEEE International Conference on Image Processing (ICIP). IEEE Publications, 2015, pp. 4773–4777.

12. U. Jayaraman, A. K. Gupta and P. Gupta, “An efficient minutiae based geometric hashing for fingerprint database,” Neurocomputing, vol. 137, pp. 115–126, 2014.

13. Z. Jin , A. B. J. Teoh, T. S. Ong and C. Tee, “A revocable fingerprint template for security and privacy preserving,” KSII Trans. Internet Inf. Syst., vol. 4, no. 6, pp. 1327–1342, 2010.

14. S. Wang and J. Hu, “Alignment-free cancelable fingerprint template design: A densely infinite-to-one mapping approach,” Pattern Recognit., vol. 45, no. 12, pp. 4129–4137, 2012.

15. A . Muñoz-Briseño, A. Gago-Alonso and J. Hernández-Palancar, “Using reference point as feature for fingerprint indexing,” in Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications, E. Bayro-Corrochano, and E. Hancock, Ed. Berlin: Springer, 2014, pp. 367–374.

16. K. Cao and A. K. Jain, “Fingerprint indexing and matching: An integrated approach,” IEEE International Joint Conference on Biometrics (IJCB), CO, USA, 2017, pp. 437–445.

17. K. Cao and A. K. Jain, “Automated latent fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 4, pp. 788–800, 2019.

18. C. C. Bai, W. Q. Wang, T. Zhao, R. X. Wang and M. Q. Li, “Deep learning compact binary codes for fingerprint indexing,” Frontiers Inf. Technol. Electronic Eng., vol. 19, no. 9, pp. 1112–1123, 2018.

19. R. Cappelli, M. Ferrara, A. Franco and D. Maltoni, “Fingerprint verification competition 2006,” Biom. Technol. Today, vol.15, no.7–8, pp.7–9, 2007.

20. K. Karacs, et al., “Software Library for Cellular Wave Computing Engines Version 3.1”, Cellular Sensory and Wave Computing Laboratory of the Computer and Automation Research Inst., Hungarian Academy of Sciences and the Jedlik Laboratories of the Pazmany P. Catholic University. Budapest, Hungary, 2010.

21. T. Ç. Mayadağli, E. Saatçi and R. Edizkan, “A CNN based rotation invariant fingerprint recognition system,” IU-JEEE, vol. 17, no. 2, pp. 3471–3479, 2017.

22. J. G. Daughman, “Uncertainty relation for resolution in space, spatialFrequency, and orientation optimized by two-dimensional visual cortical filters,” J. Opt. Soc. Am. A, vol. 2, no. 7, pp. 1160–1169, 1985.

23. L. Shen and L. Bai, “A review of gabor wavelets for face recognition,” Patt. Anal. Appl., vol. 9, pp. 273–292, 2006.

24. B. Ergen, “A fusion method of gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection,” Sci. World J., vol. 2014, pp. 1–13. London, UK: Hindawi Publishing Corporation, 2014.

25. T. J. Su, Y. Y. Du, Y. J. Cheng and Y. H. Su, “A fingerprint recognition system using cellular neural networks,” in 9th International Workshop on Cellular Neural Networks and Their Applications. Hsinchu: CNNA, 2005, pp. 170–173.

26. D. Riquelme and M. A. Akhloufi, “Deep learning for lung cancer nodules detection and classification in CT scans,” Artif. Intell., vol. 1, no. 1, pp. 28–67, 2020.

27. S. Duffner, “Face Image Analysis with Convolutional Neural Networks,” Doctoral thesis. Germany, Breisgau: The Faculty of Applied Sciences, Albert-Ludwigs University, 2007.

28. S. Wang and Y. Wang, “Fingerprint enhancement in the singular point area,” IEEE Signal Process. Lett., vol. 11, no. 1, pp. 16–19, 2004.

29. Z. E. Khatab, A. H. Gazestani, S. A. Ghorashi and M. Ghavami, “A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine,” Signal Process., vol. 181, 2021.

30. X. Tan and B. Bhanu, “Fingerprint matching by genetic algorithms,” Pattern Recognit., vol. 39, no. 3, pp. 465–477, 2006.

31. M. A. Medina-Pérez, A. M. Moreno, M. Á. F. Ballester, M. GarcíaBorroto, O. Loyola-González and L. Altamirano-Robles, “Latent fingerprint identification using deformable minutiae clustering,” Neurocomputing, vol. 175, pp. 851–865, 2016.