Abstract
Sweat pores have been proved to be discriminative and successfully used for automatic fingerprint recognition. It is crucial to extract pores precisely to achieve high recognition accuracy. To extract pores accurately and robustly, we propose a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation. More specifically, pore candidates are coarsely estimated using logical operation at first; then, coarse pore candidates are further judged through well-trained CNN models; precise pore locations are finally refined by logical and morphological operation. The experimental results evaluated on the public dataset show that the proposed method outperforms other state-of-the-art methods in comparison.
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References
Yoon, S., Jain, A.K.: Longitudinal study of fingerprint recognition. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 112, no. 28, p. 8555 (2015)
Peishan, X., Yuzhen, Y.: HPTLC fingerprint identification of commercial ginseng drugs -reinvestigation of HPTLC of ginsenosides. J. High Resolut. Chromatogr. 10(11), 607–613 (2015)
Liu, F., Zhang, D., Shen, L.: Study on novel curvature features for 3D fingerprint recognition. Neurocomputing. 168(C), 599–608 (2015)
Thornton, J.: Setting standards in the comparison and identification. In: Proceedings of 84th Annual Training Conference of the Cali- fornia State Division of IAI. Laughlin, Nevada (2000)
Kryszczuk, K., Drygajlo, A., Morier, P.: Extraction of level 2 and level 3 features for fragmentary fingerprints. In: Proceedings of the 2nd COST275 Workshop, vol. 27, no. 2, pp. 290–304 (2004)
Jain, A.K., Chen, Y., Demirkus, M.: Pores and ridges: fingerprint matching using level 3 features. IEEE Trans. PAMI. 4(1), 477–480 (2006)
Zhao, Q., Zhang, L., Zhang, D., Luo, N.: Direct pore matching for fingerprint recognition. Adv. Biometrics ICB. 5558, 597–606 (2009)
Kryszczuk, K., Morier, P., Drygajlo, A.: Study of the distinctiveness of level 2 and level 3 features in fragmentary fingerprint comparison. In: Maltoni, D., Jain, Anil K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 124–133. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25976-3_12
Jain, A.K., 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)
Liu, F., Zhao, Q., Zhang, L., Zhang, D.: Fingerprint pore matching based on sparse representation. In: Proceedings of the 20th International Conference on Pattern Recognition (2010)
Liu, F., Zhao, Q., Zhang, D.: A novel hierarchical fingerprint matching approach. Pattern Recogn. 44(8), 1604–1613 (2011)
Liu, F., Zhao, Y., Shen, L.: Feature guided fingerprint pore matching. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 334–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_36
Zhao, Q., Zhang, D., Zhang, L., Luo, N.: Adaptive fingerprint pore modeling and extraction. Pattern Recognit. 43(8), 2833–2844 (2010)
Abhyankar, A., Schuckers, S.: Towards integrating level-3 Features with perspiration pattern for robust fingerprint recognition. In: IEEE International Conference on Image Processing, vol. 59, no. 1, pp. 3085–3088. IEEE (2010)
Malathi, S., Maheswari, S., Meena, C.: Fingerprint pore extraction based on marker controlled watershed segmentation. In: The International Conference on Computer and Automation Engineering, vol. 3, pp. 337–340. IEEE (2010)
da Silva Teixeira, R.F., Leite, N.J.: On adaptive fingerprint pore extraction. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 72–79. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39094-4_9
Xu, Y., Lu, G., Liu, F., Li, Y.: Fingerprint pore extraction based on multi-scale morphology. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 288–295. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_31
Labati, R., Genovese, A., Muñoz, E., Piuri, V., Scotti, F.: A novel pore extraction method for heterogeneous fingerprint images using convolutional neural networks. Pattern Recognit. Lett. (2017)
Wang, H., Yang, X., Ma, L., Liang, R.: fingerprint pore extraction using U-Net based fully convolutional network. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 279–287. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_30
Jang, H., Kim, D., Mun, S., Choi, S., Lee, H.: Deeppore: fingerprint pore extraction using deep convolutional neural networks. IEEE Signal Process. Lett. 24(12), 1808–1812 (2017)
Krizhevsky, A., Sutskever, I., Hinton, I.G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, vol. 60, no. 2, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J.: Caffe: convolutional architecture for fast feature embedding, pp. 675–678 (2014)
Bernsen, J.: Dynamic thresholding of grey-level images. In: International Conference on Pattern Recognition (1986)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, London (2009). https://doi.org/10.1007/978-1-84882-254-2
Lin, H., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
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Zhao, Y., Liu, F., Shen, L. (2018). Fingerprint Pore Extraction Using Convolutional Neural Networks and Logical Operation. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_5
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