References
Jain A K, Nandakumar K, Ross A. 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn Lett, 2016, 79: 80–105
Henry E R. Classification and Uses of Finger Prints. London: HM Stationery Office, 1905
Karu K, Jain A K. Fingerprint classification. Pattern Recogn, 1996, 29: 389–404
Zhang Q, Yan H. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recogn, 2004, 37: 2233–2243
Jin J W, Liu Z L, Chen C L P. Discriminative graph regularized broad learning system for image recognition. Sci China Inf Sci, 2018, 61: 112209
Li Q Q, Zou Q, Ma D, et al. Dating ancient paintings of Mogao Grottoes using deeply learnt visual codes. Sci China Inf Sci, 2018, 61: 092105
Peralta D, Triguero I, García S, et al. On the use of convolutional neural networks for robust classification of multiple fingerprint captures. Int J Intell Syst, 2018, 33: 213–230
Michelsanti D, Guichi Y, Ene A D, et al. Fast fingerprint classification with deep neural network. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018. 202–209
Tang Y, Gao F, Feng J, et al. Fingernet: an unified deep network for fingerprint minutiae extraction. In: Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB). New York: IEEE, 2017. 108–116
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 61333015).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supporting information
Appendixes A and B. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
Rights and permissions
About this article
Cite this article
Tang, Y., Li, R., Liu, Y. et al. FClassNet: a fingerprint classification network integrated with the domain knowledge. Sci. China Inf. Sci. 62, 229102 (2019). https://doi.org/10.1007/s11432-019-9930-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-019-9930-4