A CNN Based Rotation Invariant Fingerprint Recognition System

Öz

This paper presents a Cellular Neural Networks (CNN) based rotation invariant fingerprint recognition system by keeping the hardware implementability in mind. Core point was used as a reference point and detection of the core point was implemented in the CNN framework. Proposed system consists of four stages: preprocessing, feature extraction, false feature elimination and matching. Preprocessing enhances the input fingerprint image. Feature extraction creates rotation invariant features by using core point as a reference point. False feature elimination increases the system performance by removing the false minutiae points. Matching stage compares extracted features and creates a matching score. Recognition performance of the proposed system has been tested by using high resolution PolyU HRF DBII database. The results are very encouraging for implementing a CNN based fully automatic rotation invariant fingerprint recognition system.

Anahtar Kelimeler

• Fingerprint,Cellular Neural Networks,Rotation Invariant,Fingerprint Recognition System

Kaynakça

1. [1] Q. Gao, S. Moschytz, “Fingerprint Feature Extraction Using CNNs”, in Proceedings of European Conference on Circuit Theory and Design 2001, Espoo, Finland, 2001, pp. 28-31.
2. [2] T. Su , Y. Du, Y. Cheng, Y. Su, “Fingerprint Recognition System Using Cellular Neural Network”, in Proceedings of 9th International Workshop on Cellular Neural Networks and their Applications, Hsinchu, Taiwan, 2005, pp. 170-173.
3. [3] I. Kale, R. Abrishambaf, H. Demirel, “A Fully CNN Based Fingerprint Recognition System”, in Proceedings of 11th International Workshop on Cellular Neural Networks and Their Applications, Santiago de Compostela, Spain, 2008, pp. 14-16.
4. [4] L. O. Chua, L. Yang, “Cellular neural networks: Theory”, IEEE T CIRCUITS SYST, vol. 35, pp. 1257-1272, 1988.
5. [5] M. D. Doan, M. Glenser, R. Chakrabaty, M. Heidenreich, S. Cheung, “Realization of a Digital Cellular Neural Network for Image Processing”, in Proceedings of Third International Workshop on Cellular Neural Networks and Their Applications, Rome, Italy, 1994, pp. 85-90.
6. [6] E. Saatci, “Image Processing Using Cellular Neural Networks”, PhD Thesis, London South Bank University, London, UK, 2003.
7. [7] L. O. Chua, L. Yang, “Cellular Neural Networks: Applications”, IEEE T CIRCUITS SYST, vol. 35, pp. 1273-1290, 1988.
8. [8] K. R. Crounce, L. O. Chua, “Methods for Image Processing and Pattern Formation in Cellular Neural Networks: A Tutorial”, IEEE T CIRCUITS-I, vol. 42, pp. 583-601, 1995.

9. [9] Z. Nagy, P. Szolgay, “Configurable multilayer CNN-UM emulator on FPGA”, IEEE T CIRCUITS-I, vol. 50, pp. 774-778, 2003.
10. [10] J. Javier Martinez, F. Javier Toledo, E. Fernandez, J. M. Ferrandez, “A retinomorphic architecture based on discretetime cellular neural networks using reconfigurable computing”, NEUROCOMPUTING, vol. 71, pp. 766-775, 2008.
11. [11] K. Kayaer, V. Tavsanoglu, “A new approach to emulate CNN on FPGAs for real time video processing”, in Proceedings of 11th International Workshop on Cellular Neural Networks and Their Applications, Santiago de Compostela, Spain, 2008, pp. 23-28.
12. [12] N. Yildiz, E. Cesur, K. Kayaer, V. Tavsanoglu, M. Alpay, “Architecture of a Fully Pipelined Real-Time Cellular Neural Network Emulator”, IEEE T CIRCUITS-I, vol. 62, pp. 130-138, 2015.
13. [13] E. Saatci, V. Tavsanoglu, “Fingerprint Image Enhancement Using CNN Filtering Techniques”, INT J NEURAL SYST, vol. 13, pp. 453-460, 2003.
14. [14] J. G. Daughman, “Uncertainty Relation for Resolution in Space, Spatial-Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters”, J OPT SOC AM, vol. 2, pp. 1160-1169, 1985.
15. [15] B. E. Shi, “Gabor-type filtering in space and time with Cellular Neural Networks”, IEEE T CIRCUITS-I, vol. 45, pp. 121-132, 1998.
16. [16] C. Rekeczky, “Dynamic spatio-temporal nonlinear filtering and detection on CNN archiecture - theory, modeling and applications”, PhD Thesis, Computer and Automation Institute Hungarian Academy of Sciences, Hungary, 1999.
17. [17] K. Karacs, G. Cserey, A. Zarandy, P. Szolgay, C. Rekeczky, L. Kek, V. Szabo, G. Pazienza, T. Roska, “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.
18. [18] K. Nilsson, J. Bigun, “Localization of corresponding points in fingerprints by complex filtering”, PATTERN RECOGN LETT, vol. 24, pp. 2135-2144, 2003.
19. [19] N. K. Jalutharia, “Fingerprint Recognition and Analysis”, MSc Thesis, Thapar University, India, 2010.
20. [20] D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, “Handbook of fingerprint recognition”, Springer, New York, USA, 2003.
21. [21] The PolyU HRF Database II, Available at: http://www4. comp.polyu.edu.hk/˜biometrics/HRF/HRF_old.htm Accessed 10 May 2017.
22. [22] P. J. Phillips, A. Martin, C. L. Wilson, M. Przybocky, “An introduction to evaluating biometric systems”, COMPUTER, vol. 33, pp. 56-63, 2000.
23. [23] UK Government’s Biometrics Working Group, “Best practices in testing and reporting performance of biometric devices”, v2.01, 2002.
24. [24] D. Maio, D. Maltoni, R. Capelli, J. L. Wayman, A. K. Jain, “Fvc2000: Fingerprint verification competition”, IEEE T PATTERN ANAL, vol. 24, pp. 402-412, 2002.
25. [25] R. Capelli, D. Maio, D. Maltoni, J. L. Wayman, A. K. Jain, “Performance evaluation of fingerprint verification systems”, IEEE T PATTERN ANAL, vol. 28, pp. 3-18, 2006.
26. [26] Q. Zhao, D. Zhang, L. Zhang, N. Luo, “Adaptive fingerprint pore modeling and extraction”, PATTERN RECOGN, vol. 43, pp. 2833-2844, 2010.
27. [27] N. A. Mngenge, “An Adaptive Quality-Based Fingerprints Matching Using Feature Level 2 (Minutiae) and Extended Features (Pores)”, M.Ing Degree Thesis, University of Johannesburg, South Africa, 2013.