Abstract
Most of the existing conventional methods for singular points detection of fingerprints depend on the orientation fields of fingerprints, which cannot achieve the reliable and accurate detection of poor quality fingerprints. In this paper, a novel algorithm is proposed for fingerprint singular points detection, which combines multi-scaling fully convolutional networks (FCN) and probability model. Firstly, we divide fingerprint image into overlapping blocks and pose them into a classification problem. And we propose a convolutional neural network (ConvNet) based approach for estimating whether the center of a block is one singularity point. Then, we transform the ConvNet into FCN and fine-tuned. Finally, we adopt probabilistic methods to determine the actual positions of singular points. The performance testing was conducted on NIST DB4 and FVC2002 DB1 database, which concluded that the proposed algorithm gives better results than competing approaches.
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References
Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999)
Chan, K.C., Moon, Y.S., Cheng, P.S.: Fast fingerprint verification using subregions of fingerprint images. IEEE Trans. Circuits Syst. Video Technol. 14(1), 95–101 (2004)
Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recogn. 17(3), 295–303 (1984)
Gu, J., Zhou, F. Chen, F.: A novel algorithm for detecting singular points from fingerprint images. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1239–1250 (2009)
Fan, L., Wang, S., Wang, H., Guo, T.: Singular points detection based on zero-pole model in fingerprint images. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 929–940 (2008)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Krizhevsky, A., Sutskever, I.: Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25(1), 1097–1105 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Watson, C.I., Wilson, C.L.: Nist special database 4 (1992)
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2002: second fingerprint verification competition. In: Proceedings of 16th International Conference on Pattern recognition, vol. 3, pp. 811–814. IEEE (2002)
Chikkerur, S., Ratha, N.: Impact of singular point detection on fingerprint matching performance. In: Fourth IEEE Workshop on Technologies, Automatic Identification Advanced, pp. 207–212. IEEE (2005)
Acknowledgments
This work was funded by the Chinese National Natural Science Foundation (11331012, 11571014).
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Qin, J., Han, C., Bai, C., Guo, T. (2017). Multi-scaling Detection of Singular Points Based on Fully Convolutional Networks in Fingerprint Images. 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_24
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DOI: https://doi.org/10.1007/978-3-319-69923-3_24
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