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IJSTR >> Volume 8 - Issue 7, July 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Convolutional Neural Network Model Robust To Distorted Fingerprints

[Full Text]



Ogechukwu Iloanusi, Chimdalu Okide



biometrics, convolutional neural network, distortion, fingerprint.



The greatest challenge in fingerprint recognition is verifying distorted fingerprints. Distortion in fingerprints may arise from errors introduced while acquiring fingerprints, the nature of the fingerprints or from how there were deposited (in the case of latent fingerprints). In this paper, two convolutional neural networks were trained using different approaches. The first was trained in a regular pattern while the second was trained with an approach that minimizes errors that arise from verifying distorted fingerprints, and hence proposed for training models to be robust to distorted fingerprints. The trained models were evaluated on good and distorted data-sets. Results are modest and show better performance in the second model, compared to the first.



[1] S. Gu, J. Feng, J. Lu, and J. Zhou, “Efficient Rectification of Distorted Fingerprints,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 1, pp. 156–169, Jan. 2018.
[2] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed. London: Springer-Verlag, 2009.
[3] A. M. Bazen and S. H. Gerez, “Achievements and Challenges in Fingerprint Recognition,” in Biometric Solutions, Boston, MA: Springer US, 2002, pp. 23–57.
[4] O. N. Iloanusi and C. A. Ezema, “A quantitative impact of fingerprint distortion on recognition performance,” Inf. Secur. J. A Glob. Perspect., vol. 26, no. 6, pp. 267–275, Nov. 2017.
[5] N. D. Kalka and R. A. Hicklin, “On relative distortion in fingerprint comparison,” Forensic Sci. Int., vol. 244, pp. 78–84, Nov. 2014.
[6] H. D. Sheets, A. Torres, G. Langenburg, P. J. Bush, and M. A. Bush, “Distortion in Fingerprints: A Statistical Investigation using Shape Measurement Tools,” J. Forensic Sci., vol. 59, no. 4, pp. 1113–1120, Jul. 2014.
[7] J. Wallis and J. Goulet, “Quantitative analysis of the distortion of friction ridge impressions according to three deposition pressure levels and horizontal movement,” J. Forensic Identif., vol. 67, no. 2, pp. 259–277, 2017.
[8] C. S. Mlambo and Y. Moolla, “Complexity and Distortion Analysis on Methods for Unrolling 3D to 2D Fingerprints,” in 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015, pp. 103–109.
[9] C. S. Mlambo and M. B. Shabalala, “Distortion Analysis on Binary Representation of Minutiae Based Fingerprint Matching for Match-on-Card,” in 2015 IEEE Symposium Series on Computational Intelligence, 2015, pp. 349–353.
[10] Qijun Zhao, A. Jain, and G. Abramovich, “3D to 2D fingerprints: Unrolling and distortion correction,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–8.
[11] W. Lee, S. Cho, H. Choi, and J. Kim, “Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners,” Expert Syst. Appl., vol. 87, pp. 183–198, Nov. 2017.
[12] K. Madhavi and B. Sreenath, “Rectification of distortion in single rolled fingerprint,” in 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), 2016, pp. 1–4.
[13] Y. Li and L. Guo, “Robust Image Fingerprinting via Distortion-Resistant Sparse Coding,” IEEE Signal Process. Lett., vol. 25, no. 1, pp. 140–144, Jan. 2018.
[14] E. Derman and M. Keskinoz, “Normalized cross-correlation based global distortion correction in fingerprint image matching,” in 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), 2016, pp. 1–4.
[15] S. Kundu and G. Sarker, “A modified BP network using malsburg learning for rotation and location invariant fingerprint recognition and localization with and without occlusion,” in 2014 Seventh International Conference on Contemporary Computing (IC3), 2014, pp. 617–623.
[16] R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2128–2141, Dec. 2010.
[17] R. Shanthi and T. A. Kumaran, “Enhanced fingerprint distortion removal system,” in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 1859–1863.
[18] C.-C. Liao and C.-T. Chiu, “Fingerprint recognition with ridge features and minutiae on distortion,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 2109–2113.
[19] J. Zouari and M. Hamdi, “Enhanced fingerprint fuzzy vault based on distortion invariant minutiae structures,” in 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2016, pp. 491–495.
[20] O. N. Iloanusi, “Fusion of finger types for fingerprint indexing using minutiae quadruplets,” Pattern Recognit. Lett., vol. 38, no. 1, pp. 8–14, 2014.
[21] D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain, “FVC2000: fingerprint verification competition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 402–412, Mar. 2002.
[22] D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain, “FVC2002: Second Fingerprint Verification Competition,” in Object recognition supported by user interaction for service robots, 2002, vol. 3, pp. 811–814.
[23] D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain, “FVC2004: Third Fingerprint Verification Competition,” Springer, Berlin, Heidelberg, 2004, pp. 1–7.