Abstract
The public demand for personal safety is increasing rapidly. Fingerprint features as the most commonly used bio-signature need to improve their safety continuously. The third level features of fingerprint (especially the sweat pores) can be added to the automatic fingerprint recognition system to increase the accuracy of fingerprint identification in a variety of environments. Due to perspiration activities, the shape and size sweat of pores are varying spatially and temporally. Extraction of fingerprint pores is both critical and challenging. In this paper, we adapt a novel fully convolutional neural network called U-net for ridges and sweat pores extraction. The PolyU High-Resolution-Fingerprint (HRF) database is used for testing of the proposed method. The results show the validity of the proposed method. With the majority of the pores correctly extracted, the proposed method can serve for fingerprint recognition using Level 3 features.
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References
Pankanti, S., Prabhakar, S., Jain, A.: On the individuality of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1010–1025 (2002)
Maltoni, D., Maio, D., Jain, A.: Handbook of Fingerprint Recognition. Springer, Dordrecht (2006)
Zhang, D., Liu, F., Zhao, Q., Lu, G., Luo, N.: Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Trans. Instrum. Meas. 60(3), 863–871 (2011)
Roddy, A., Stosz, J.: Fingerprint features-statistical analysis and system performance estimates. Proc. IEEE 85(9), 1390–1421 (1997)
Parsons, N., Smith, J., Thonnes, E., Wang, L., Wilson, R.: Rotationally invariant statistics for examining the evidence from the pores in fingerprints. Law Probab. Risk 7(1), 1–14 (2007)
Jain, A., Chen, Y., Demirkus, M.: Pores and ridges: high-resolution fingerprint matching using level 3 features. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 15–27 (2007)
Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press Inc., Boca Raton (1999)
Kryszczuk, K.M., Morier, P., Drygajlo, A.: Study of the distinctiveness of level 2 and level 3 features in fragmentary fingerprint comparison. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 124–133. Springer, Heidelberg (2004). doi:10.1007/978-3-540-25976-3_12
Ray, M., Meenen, P., Adhami, R.: A novel approach to fingerprint pore extraction. In: Proceedings of the 37th South-Eastern Symposium on System Theory, SSST, pp. 282–286 (2005)
Zhao, Q., Zhang, D., Zhang, L., Luo, N.: Adaptive fingerprint pore modeling and extraction. Pattern Recogn. 43(8), 2833–2844 (2010)
Genovese, A., Munoz, E., Piuri, V., Scotti, F., Sforza, G.: Towards touchless pore fingerprint biometrics: a neural approach. In: IEEE Congress on Evolutionary Computation (CEC). IEEE (2016)
Labati, R.D., Genovese, A., Muñoz, E., Piuri, V., Scotti, F.: A novel pore extraction method for heterogeneous fingerprint images using convolutional neural networks. Pattern Recogn. Lett. (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 79, pp. 3431–3440. IEEE (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_28
Snelick, R., Indovina, M., Yen, J., Mink, A.: Multimodal biometrics: issues in design and testing. In: Proceedings of the 5th International Conference on Multimodal Interfaces, ICMI, Vancouver, pp. 68–72 (2003)
Zhao, Q., Zhang, L., Zhang, D., Luo, N.: Direct pore matching for fingerprint recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 597–606. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01793-3_61
Acknowledgments
This research is partially supported by Natural Science Foundation of China (61602414, 61402411).
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Wang, H., Yang, X., Ma, L., Liang, R. (2017). Fingerprint Pore Extraction Using U-Net Based Fully Convolutional Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_30
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DOI: https://doi.org/10.1007/978-3-319-69923-3_30
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