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http://dx.doi.org/10.17703/JCCT.2022.8.5.653

Research Trends in CNN-based Fingerprint Classification  

Jung, Hye-Wuk (Dept. of College of Liberal Arts and Interdisciplinary Studies, Kyonggi University)
Publication Information
The Journal of the Convergence on Culture Technology / v.8, no.5, 2022 , pp. 653-662 More about this Journal
Abstract
Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.
Keywords
Fingerprint Classification; Pattern Recognition; Feature Extraction; CNN; Deep Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 T. Zia, M. Ghafoor, S.A. Tariq, and I.A. Taj, "Robust fingerprint classification with Bayesian convolutional networks," IET Image Process, Vol. 13, pp. 1280-1288, 2019. DOI: https://doi.org/10.1049/IET-IPR.2018.5466   DOI
2 https://www.nist.gov/itl/iad/image-group/nist-special-database-300
3 http://bias.csr.unibo.it/fvc2004/
4 Zabala-Blanco D, Mora M, Barrientos RJ, Hernandez-Garcia R, and Naranjo-Torres J, "Fingerprint Classification through Standard and Weighted Extreme Learning Machines," Applied Sciences, Vol. 10, No. 12, 4125, 2020. DOI: https://doi.org/10.3390/app10124125   DOI
5 Y. LeCun, K. Kavukcuoglu, and C. Farabet, "Convolutional networks and applications in vision," Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253-256, 2010. DOI: https://doi.org/10.1109/ISCAS.2010.5537907   DOI
6 A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet classification with deep convolutional neural networks," In NIPS, 2012.
7 K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
8 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90   DOI
9 D. Peralta, I. Triguero, S. Garcia, Y. Saeys, J.M. Benitez, and F. Herrera, "On the use of convolutional neural networks for robust classification of multiple fingerprint captures," International Journal of Intelligent Systems, Vol. 33, pp. 213-230, 2018. DOI: https://doi.org/10.1002/int.21948   DOI
10 D. El Hamdi, I. Elouedi, A. Fathallah, Mai K. Nguyen, and A. Hamouda, "Fingerprint Classification Using Conic Radon Transform and Convolutional Neural Networks," International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, pp. 402-413, 2018. DOI: https://doi.org/10.1007/978-3-030-01449-0_34   DOI
11 F. Wu, J. Zhu, and X. Guo, "Fingerprint pattern identification and classification approach based on convolutional neural networks," Neural Computing and Applications, Vol. 32, pp. 5725-5734, 2020. DOI: https://doi.org/10.1007/s00521-019-04499-w   DOI
12 R. Cappelli, D. Maio, and D. Maltoni, "Synthetic fingerprint-database generation," 2002 International Conference on Pattern Recognition, Vol. 3, pp. 744-747, 2002. DOI: https://doi.org/10.1109/ICPR.2002.1048096   DOI
13 D. Michelsanti, A.D. Ene, Y. Guichi, R. Stef, K. Nasrollahi, and T.B. Moeslund, "Fast fingerprint classification with deep neural networks," 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pp. 202-209, 2017. DOI: https://doi.org/10.5220/0006116502020209   DOI
14 C. Szegedy et al., "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298594   DOI
15 S. Ge, C. Bai, Y. Liu, Y. Liu, and T. Zhao, "Deep and discriminative feature learning for fingerprint classification," 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1942-1946, 2017. DOI: https://doi.org/10.1109/CompComm.2017.8322877   DOI
16 Nur-A-Alam, M. Ahsan, M.A. Based, J. Haider, M. Kowalski, "An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning," Computers and Electrical Engineering, Vol. 95, 107387, 2021. DOI: https://doi.org/10.1016/j.compeleceng.2021.107387   DOI
17 https://www.nist.gov/srd/nist-special-database-4
18 http://bias.csr.unibo.it/fvc2000/
19 http://bias.csr.unibo.it/fvc2002/
20 C. Militello, L. Rundo, S. Vitabile, and V. Conti, "Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons," Symmetry 2021, Vol. 13, No. 5, 750, 2021. DOI: https://doi.org/10.3390/sym13050750   DOI
21 J. M. Shrein, "Fingerprint classification using convolutional neural networks and ridge orientation images," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8, 2017. DOI: https://doi.org/10.1109/SSCI.2017.8285375   DOI
22 D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, 2009.
23 H. W. Jung, L, Seung, "Technical Trend Analysis of Fingerprint Classification," The Journal of the Korea Contents Association, Vol. 17, No. 9, pp. 132-144, 2017. DOI: https://doi.org/10.5392/JKCA.2017.17.09.132   DOI
24 H. W. Jung, J. H, Lee, "Noisy and incomplete fingerprint classification using local ridge distribution models," Pattern Recognition, Vol. 48, No. 2, pp. 473-484, 2015. DOI: https://doi.org/10.1016/j.patcog.2014.07.030   DOI