Skip to main content
Log in

Fingerprint image super-resolution based on multi-class deep dictionary learning and ridge prior

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The identification of low-resolution fingerprints has always been one of the focuses in the field of biometric identification. This paper proposes a method for super-resolving low-resolution fingerprints based on deep dictionary learning. First, it is necessary to obtain a priori based on the fingerprint ridge orientation. After obtaining it, the ridges of the fingerprint are divided into n categories according to the direction. Each class uses deep dictionary learning models to train corresponding high- and low-resolution dictionaries respectively. In the super-resolution part, after extracting features from the patches that require super-resolution, the sparse coefficients are obtained through the deep dictionary learning model, and then combined with the high-resolution dictionary to obtain high-resolution patches, which are combined into high-resolution fingerprints. Experimental results show that the proposed method performs better than some other methods.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Yoon, S., Feng, J., Jain, A. K.: On latent fingerprint enhancement. In: Biometric Technology for Human Identification VII, vol. 7667, pp. 60–69. SPIE (2010)

  2. Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press (1999)

    Book  Google Scholar 

  3. Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)

    Article  Google Scholar 

  4. Wei, Z., Ma, K.K.: Contrast-guided image interpolation. IEEE Trans. Image Process. 22(11), 4271–4285 (2013)

    Article  MathSciNet  Google Scholar 

  5. Zhang, K., Gool, L. V., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3217–3226. (2020)

  6. Yang, J., Wright, J., Huang, T., et al.: Image super-resolution as sparse representation of raw image patches. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)

  7. Saharia, C., Ho, J., Chan, W., et al.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713–4726 (2022)

    Google Scholar 

  8. Zhang, X., Zeng, H., Guo, S., et al.: Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision, pp. 649–667. Springer Nature Switzerland, Cham (2022)

  9. Wang, P., Bayram, B., Sertel, E.: A comprehensive review on deep learning based remote sensing image super-resolution methods. Earth Sci. Rev. 232, 104110 (2022)

    Article  Google Scholar 

  10. Liu, H., Ruan, Z., Zhao, P., et al.: Video super-resolution based on deep learning: a comprehensive survey. Artif. Intell. Rev. 55(8), 5981–6035 (2022)

    Article  Google Scholar 

  11. Lu, T., Wang, J., Zhang, Y., et al.: Satellite image super-resolution via multi-scale residual deep neural network. Remote Sens. 11(13), 1588 (2019)

    Article  Google Scholar 

  12. Tariyal, S., Majumdar, A., Singh, R., et al.: Deep dictionary learning. IEEE Access 4, 10096–10109 (2016)

    Article  Google Scholar 

  13. Mahdizadehaghdam, S., Panahi, A., Krim, H., et al.: Deep dictionary learning: a parametric network approach. IEEE Trans. Image Process. 28(10), 4790–4802 (2019)

    Article  MathSciNet  Google Scholar 

  14. Song, J., Xie, X., Shi, G., et al.: Multi-layer discriminative dictionary learning with locality constraint for image classification. Pattern Recogn. 91, 135–146 (2019)

    Article  Google Scholar 

  15. Tang, H., Liu, H., Xiao, W., et al.: When dictionary learning meets deep learning: deep dictionary learning and coding network for image recognition with limited data. IEEE Trans Neural Netw. Learn. Syst. 32(5), 2129–2141 (2020)

    Article  MathSciNet  Google Scholar 

  16. Montazeri, A., Shamsi, M., Dianat, R.: MLK-SVD, the new approach in deep dictionary learning. Vis. Comput. 37, 707–715 (2021)

    Article  Google Scholar 

  17. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  18. Scetbon, M., Elad, M., Milanfar, P.: Deep k-svd denoising. IEEE Trans. Image Process. 30, 5944–5955 (2021)

    Article  MathSciNet  Google Scholar 

  19. Huang, J. J., Dragotti, P. L.: A deep dictionary model for image super-resolution. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6777–6781. IEEE (2018)

  20. Singhal, V., Majumdar, A.: A domain adaptation approach to solve inverse problems in imaging via coupled deep dictionary learning. Pattern Recogn. 100, 107163 (2020)

    Article  Google Scholar 

  21. Vella, M., Mota, J.F.C.: Robust single-image super-resolution via CNNs and TV-TV minimization. IEEE Trans. Image Process. 30, 7830–7841 (2021)

    Article  Google Scholar 

  22. Lu, Z., Li, J., Liu, H., et al.: Transformer for single image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 457–466. (2022)

  23. Li, H., Yang, Y., Chang, M., et al.: Srdiff: single image super-resolution with diffusion probabilistic models. Neurocomputing 479, 47–59 (2022)

    Article  Google Scholar 

  24. Singh, K., Gupta, A., Kapoor, R.: Fingerprint image super-resolution via ridge orientation-based clustered coupled sparse dictionaries. J. Electron. Imaging 24(4), 043015–043015 (2015)

    Article  Google Scholar 

  25. Bian, W., Ding, S., Xue, Y.: Fingerprint image super resolution using sparse representation with ridge pattern prior by classification coupled dictionaries. IET Biom. 6(5), 342–350 (2017)

    Article  Google Scholar 

  26. Kass, M., Witkin, A.: Analyzing oriented patterns. Comput. Vis. Graphics Image Process. 37(3), 362–385 (1987)

    Article  Google Scholar 

  27. Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 905–919 (2002)

    Article  Google Scholar 

  28. Singla, N., Kaur, M., Sofat, S.: Automated latent fingerprint identification system: a review. Forensic Sci. Int. 309, 110187 (2020)

    Article  Google Scholar 

  29. Duan, Y., Feng, J., Lu, J., et al.: Estimating fingerprint pose via dense voting. IEEE Trans. Inf. Forensics Secur. 18, 2493 (2023)

    Article  Google Scholar 

  30. Tu, Y., Yao, Z., Xu, J., et al.: Fingerprint restoration using cubic Bezier curve. BMC Bioinf. 21, 1–19 (2020)

    Article  Google Scholar 

  31. Mei, Y., Cao, G., Sun, H., et al.: A systematic gradient-based method for the computation of fingerprint’s orientation field. Comput. Electr. Eng. 38(5), 1035–1046 (2012)

    Article  Google Scholar 

  32. Bian, W., Luo, Y., Xu, D., et al.: Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables. Pattern Recogn. 47(10), 3304–3313 (2014)

    Article  Google Scholar 

  33. Veshki, F.G., Vorobyov, S.A.: An efficient coupled dictionary learning method. IEEE Signal Process. Lett. 26(10), 1441–1445 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

The work is partially supported by Natural Science Foundation of Anhui Provincial (Grant No. 2108085MF206) and National Natural Science Foundation of China (Grant No. 61976006).

Author information

Authors and Affiliations

Authors

Contributions

Bian contributed to the conception of the study, revised the manuscript and provided founding support; Huang contributed significantly to analysis and wrote the manuscript; Xu performed the data analyses and manuscript preparation; Jie helped perform the analysis with constructive discussions and provided founding support; Luo performed the experiments. All authors reviewed the manuscript.

Corresponding author

Correspondence to Weixin Bian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Huang, Y., Bian, W., Xu, D. et al. Fingerprint image super-resolution based on multi-class deep dictionary learning and ridge prior. SIViP 18, 5491–5501 (2024). https://doi.org/10.1007/s11760-024-03249-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03249-3

Keywords

Navigation