[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

A Fast and Light Fingerprint-Matching Model Based on Deep Learning Approaches

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Nowadays, biometric identification has become very important due to the need to identify people in different places and devices. Among these features, the fingerprint has received more attention than others because of its biometric criteria and the ability to use easily and quickly. Neural network-based methods received considerable attention due to their high accuracy and performance. These methods also do not need data preprocessing and image segmentation. In identification systems, hardware implementation capability is critical. This paper proposes a novel convolutional neural network architecture for identification using fingerprints. The proposed architecture in this paper offers more than 94% accuracy using different databases. Also, by reducing the number of parameters and memory used by more than 75% compared to state-of-the-art counterparts and the number of convolutional layers, the proposed architecture is hardware friendly and offers at least 10% better speed than the state-of-the-art counterparts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Pandya, B., Cosma, G., Alani, A. A., Taherkhani, A., Bharadi, V., & McGinnity, T. M. (2018). “Fingerprint classification using a deep convolutional neural network,“ presented at the 4th International Conference on Information Management (ICIM), 2018.

  2. Buriro, A., Gupta, S., Yautsiukhin, A., & Crispo, B. (2021). Risk-driven behavioral biometric-based one-shot-cum-continuous user authentication Scheme. Journal of Signal Processing Systems, 93(9), 989–1006. https://doi.org/10.1007/s11265-021-01654-2.

    Article  Google Scholar 

  3. Garg, M., Arora, A., & Gupta, S. (2021). An efficient human identification through Iris Recognition System. Journal of Signal Processing Systems, 93(6), 701–708. https://doi.org/10.1007/s11265-021-01646-2.

    Article  Google Scholar 

  4. Sabri, M., Moin, M. S., & Razzazi, F. (2018). A New Framework for Match on Card and Match on host quality based Multimodal Biometric authentication. Journal of Signal Processing Systems, 91(2), 163–177. https://doi.org/10.1007/s11265-018-1385-4.

    Article  Google Scholar 

  5. Barrenechea, M., Altuna, J., Mendicute, M., Ser, J. D., & Low-Cost, A. (2009). FPGA-Based Embedded Fingerprint Verification and Matching System,“ in Intelligent Technical Systems, (Lecture Notes in Electrical Engineering, ch. Chapter 18, pp. 247–260.

  6. Dakhil, I. G., & Ibrahim, A. A. (2018). Design and implementation of Fingerprint Identification System based on KNN neural network. Journal of Computer and Communications, 06(03), 1–18. https://doi.org/10.4236/jcc.2018.63001.

    Article  Google Scholar 

  7. Engelsma, J. J., Cao, K., & Jain, A. K. (2021). Learning a fixed-length fingerprint representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6), 1981–1997. https://doi.org/10.1109/tpami.2019.2961349.

    Article  Google Scholar 

  8. Michelucci, U. (2019). Advanced Applied Deep Learning.

  9. Amirany, A., Moaiyeri, M. H., & Jafari, K. (2022). Nonvolatile associative memory design based on Spintronic Synapses and CNTFET neurons. IEEE Transactions on Emerging Topics in Computing, 10(1), 428–437. https://doi.org/10.1109/tetc.2020.3026179.

    Article  Google Scholar 

  10. Amirany, A., Epperson, G., Patooghy, A., & Rajaei, R. (2021). Accuracy adaptive spintronic adder for image Processing Applications. IEEE Transactions on Magnetics, 1–1. https://doi.org/10.1109/tmag.2021.3069161.

  11. Mahmoodpour, M., Amirany, A., Moaiyeri, M. H., & Jafari, K. (2022). “A Learning Based Contrast Specific no Reference Image Quality Assessment Algorithm,“ presented at the 2022 International Conference on Machine Vision and Image Processing (MVIP),

  12. Amirany, A., Meghdadi, M., Moaiyeri, M. H., & Jafari, K. (2021). “Stochastic Spintronic Neuron with Application to Image Binarization,“ presented at the 2021 26th International Computer Conference, Computer Society of Iran (CSICC),

  13. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

  14. Amirany, A., Jafari, K., & Moaiyeri, M. H. (2021). A Task-Schedulable nonvolatile spintronic field-programmable gate array. IEEE Magnetics Letters, 12, 1–4. https://doi.org/10.1109/lmag.2021.3092995.

    Article  Google Scholar 

  15. Kalms, L., Rad, P. A., Ali, M., Iskander, A., & Göhringer, D. (2021). A Parametrizable High-Level Synthesis Library for accelerating neural networks on FPGAs. Journal of Signal Processing Systems, 93(5), 513–529. https://doi.org/10.1007/s11265-021-01651-5.

    Article  Google Scholar 

  16. Ahmadinejad, M., Taheri, N., & Moaiyeri, M. H. (2020). Energy-efficient magnetic approximate full adder with spin-hall assistance for signal processing applications. Analog Integrated Circuits and Signal Processing, 102(3), 645–657. https://doi.org/10.1007/s10470-020-01630-z.

    Article  Google Scholar 

  17. Taheri, N., Manely, A., Pang, A. R., & Alian, M. (2022). “Profiling an Architectural Simulator,“ presented at the 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS),

  18. Khaledyan, D., Amirany, A., Jafari, K., Moaiyeri, M. H., Khuzani, A. Z., & Mashhadi, N. (2020). “Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution,“ presented at the 2020 IEEE Global Humanitarian Technology Conference (GHTC),

  19. Amirany, A., Jafari, K., & Moaiyeri, M. H. (2022). Double data rate magnetic RAM for efficient Artificial Intelligence and Cache Applications. IEEE Transactions on Magnetics, 1–1. https://doi.org/10.1109/tmag.2022.3162030.

  20. BahmanAbadi, M., Amirany, A., Jafari, K., & Moaiyeri, M. H. (2022). Efficient and highly Reliable Spintronic non-volatile quaternary memory based on Carbon Nanotube FETs and Multi-TMR MTJs. ECS Journal of Solid State Science and Technology. https://doi.org/10.1149/2162-8777/ac77bb.

    Article  Google Scholar 

  21. Kosarirad, H., Ghasempour Nejati, M., Saffari, A., Khishe, M., Mohammadi, M., & Du, S. (2022). “Feature Selection and Training Multilayer Perceptron Neural Networks Using Grasshopper Optimization Algorithm for Design Optimal Classifier of Big Data Sonar,“ Journal of Sensors, vol. pp. 1–14, 2022, doi: https://doi.org/10.1155/2022/9620555.

  22. Dincă Lăzărescu, A. M., Moldovanu, S., & Moraru, L. (2022). “A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks,“ Inventions, vol. 7, no. 2, doi: https://doi.org/10.3390/inventions7020039.

  23. Mohamed, M. H. (2021). Fingerprint classification using deep convolutional neural network. Journal of Electrical and Electronic Engineering, 9(5), https://doi.org/10.11648/j.jeee.20210905.11.

  24. An Introduction to Neural Networks. Taylor \\& Francis, Inc., 1997, p. 288.

  25. Mazlan, A. B., Ng, Y. H., & Tan, C. K. (2022). A fast indoor positioning using a Knowledge-Distilled Convolutional neural network (KD-CNN). Ieee Access : Practical Innovations, Open Solutions, 10, 65326–65338. https://doi.org/10.1109/access.2022.3183113.

    Article  Google Scholar 

  26. Liu, Y., Zhou, B., Han, C., Guo, T., & Qin, J. (2019). A novel method based on deep learning for aligned fingerprints matching. Applied Intelligence, 50(2), 397–416. https://doi.org/10.1007/s10489-019-01530-4.

    Article  Google Scholar 

  27. Militello, C., Rundo, L., Vitabile, S., & Conti, V. (2021). “Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons,“ Symmetry, vol. 13, no. 5, doi: https://doi.org/10.3390/sym13050750.

  28. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  29. Gunawan, T. S., et al. (2020). Development of video-based emotion recognition using deep learning with Google Colab. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(5), https://doi.org/10.12928/telkomnika.v18i5.16717.

  30. Kanani*, P., & Padole, D. M. (2019). Deep learning to detect skin Cancer using Google Colab. International Journal of Engineering and Advanced Technology, 8, 2176–2183. https://doi.org/10.35940/ijeat.F8587.088619.

  31. Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., & Jain, A. K. (2002). FVC2000: Fingerprint verification competition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3), 402–412. https://doi.org/10.1109/34.990140.

    Article  Google Scholar 

  32. Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., & Jain, A. K. (2002). “FVC2002: Second Fingerprint Verification Competition,“ presented at the Object recognition supported by user interaction for service robots,

  33. Szegedy, C. (2015). “Going deeper with convolutions,“ in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.

Download references

Funding

The authors did not receive support from any organization for the submitted work. The authors declare they have no financial interests.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kian Jafari.

Ethics declarations

Conflict of Interest

The authors have no competing interests to declare relevant to this article’s content.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafaghi, H., Kiani, M., Amirany, A. et al. A Fast and Light Fingerprint-Matching Model Based on Deep Learning Approaches. J Sign Process Syst 95, 551–558 (2023). https://doi.org/10.1007/s11265-023-01870-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-023-01870-y

Keywords

Navigation