SpringerLink
Log in

Fingerprint Classification Based on the Henry System via ResNet

  • Conference paper
  • First Online:
Systems, Signals and Image Processing (IWSSIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1527))

Included in the following conference series:

Abstract

Fingerprints are widely used for biometric validation worldwide. Since human fingerprints are unique and remain constant over time, it provides an easy-to-use, reliable, and economical authentication method. In addition to that, fingerprint recognition systems are of great importance because of their applicability in our lives. This work presents a classification methodology based on Henry Classification System using Convolutional Neural Networks (CNNs) models, such as Darknet, Alexnet, Resnet, VGG16, and Deep Belief Network. Besides that, we evaluate our proposal by carrying out experiments using grayscale images and pre-processed images as input on the classification step with the combination of the Gabor filter and the morphological thinning operation. We have obtained the highest result accuracy of 95.1\(\%\) in the NIST Special Database 4, a widespread fingerprint dataset, using the Resnet 34 model in grayscale images. The proposed approach was evaluated with extraction strategies of classic attributes and based on convolutional networks. According to the results, the proposed methodology presents promising results, surpassing the traditional approaches present in the literature.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Brazil)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (Brazil)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (Brazil)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A point in loop and whorl prints that lies within an often triangular, three-pronged, or funnel-shaped structure.

References

  1. Abbood, A.A., Sulong, G., Razzaq, A.A.A., Peters, S.U.: Segmentation and enhancement of fingerprint images based on automatic threshold calculations. In: Saeed, F., Gazem, N., Patnaik, S., Saed Balaid, A.S., Mohammed, F. (eds.) IRICT 2017. LNDECT, vol. 5, pp. 400–411. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59427-9_43

    Chapter  Google Scholar 

  2. Ahmad, F., Mohamad, D.: A review on fingerprint classification techniques. In: 2009 International Conference on Computer Technology and Development, vol. 2, pp. 411–415 (2009)

    Google Scholar 

  3. Almajmaie, L., Ucan, O.N., Bayat, O.: Fingerprint recognition system based on modified multi-connect architecture (MMCA). Cogn. Syst. Res. 58, 107–113 (2019)

    Article  Google Scholar 

  4. Cao, K., Pang, L., Liang, J., Tian, J.: Fingerprint classification by a hierarchical classifier. Pattern Recogn. 46(12), 3186–3197 (2013)

    Article  Google Scholar 

  5. Chhabra, M., Shukla, M.K., Ravulakollu, K.K.: Bagging- and boosting-based latent fingerprint image classification and segmentation. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1166, pp. 189–201. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5148-2_17

    Chapter  Google Scholar 

  6. Ding, S., Shi, S., Jia, W.: Research on fingerprint classification based on twin support vector machine. IET Image Process. 14(2), 231–235 (2020)

    Article  Google Scholar 

  7. Ezzati Khatab, Z., Hajihoseini Gazestani, A., Ghorashi, S.A., Ghavami, M.: A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine. Signal Process. 181, 107915 (2021)

    Article  Google Scholar 

  8. Gabor, D.: Theory of communication. part 1: the analysis of information. J. Inst. Electr. Eng. - Part III: Radio Commun. Eng. 93(12), 429–441 (1946)

    Google Scholar 

  9. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd Edn. Prentice-Hall, Inc., USA (2006)

    Google Scholar 

  10. Haghighat, M., Zonouz, S., Abdel-Mottaleb, M.: Identification using encrypted biometrics. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 440–448. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40246-3_55

    Chapter  Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  12. Henry, E.R.: Classification and Uses of Fingerprints. Wyman and Sons Limited (1906)

    Google Scholar 

  13. Jain, A., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999)

    Article  Google Scholar 

  14. Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recogn. 29(3), 389–404 (1996)

    Article  Google Scholar 

  15. Klimczak, L., von Eschenbach, C.E., Thompson, P., Buters, J., Mueller, G.: Applying deep learning to pollen identification using metabolic fingerprints. J. Allergy Clin. Immunol. 147(2, Supplement), AB83 (2021). programs and Abstracts of Papers to be Presented During Virtual Scientific Sessions: 2021 AAAAI Virtual Annual Meeting

    Google Scholar 

  16. Krizhevsky, A.: Convolutional deep belief networks on cifar-10 (May 2012)

    Google Scholar 

  17. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  18. Lam, L., Lee, S., Suen, C.Y.: Thinning methodologies - a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14, 869–885 (1992)

    Article  Google Scholar 

  19. Le, N.T., Wang, J.W., Le, D.H., Wang, C.C., Nguyen, T.N.: Fingerprint enhancement based on tensor of wavelet subbands for classification. IEEE Access 8, 6602–6615 (2020)

    Article  Google Scholar 

  20. Li, J., Yau, W.Y., Wang, H.: Combining singular points and orientation image information for fingerprint classification. Pattern Recogn. 41(1), 353–366 (2008)

    Article  Google Scholar 

  21. Lin Hong, Yifei Wan, Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)

    Google Scholar 

  22. Liu, M.: Fingerprint classification based on adaboost learning from singularity features. Pattern Recogn. 43(3), 1062–1070 (2010)

    Article  Google Scholar 

  23. Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730–734 (2015)

    Google Scholar 

  24. Medeiros, A.G., et al.: A novel approach for automatic enhancement of fingerprint images via deep transfer learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

    Google Scholar 

  25. Miranda, N.D., Novamizanti, L., Rizal, S.: Convolutional neural network pada klasifikasi sidik jari menggunakan resnet-50. Jurnal Teknik Informatika (Jutif) 1(2), 61–68 (2020)

    Article  Google Scholar 

  26. Redmon, J.: Darknet: open source neural networks in c. http://pjreddie.com/darknet/ (2013–2016)

  27. Rim, B., Kim, J., Hong, M.: Fingerprint classification using deep learning approach. Multimedia Tools Appl. 1–17 (2020). https://doi.org/10.1007/s11042-020-09314-6

  28. Schuch, P., Schulz, S., Busch, C.: Survey on the impact of fingerprint image enhancement. IET Biometrics 7(2), 102–115 (2018)

    Article  Google Scholar 

  29. Serafim, P.B.S., et al.: A method based on convolutional neural networks for fingerprint segmentation. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2019)

    Google Scholar 

  30. Sharma, R.P., Dey, S.: Two-stage quality adaptive fingerprint image enhancement using fuzzy c-means clustering based fingerprint quality analysis. Image Vis. Comput. 83–84, 1–16 (2019)

    Article  Google Scholar 

  31. Vasavi, S., Priyadarshini, N.K., Vardhan, K.H.: Invariant feature based darknet architecture for moving object classification. IEEE Sens. J. 21(10), 11417–11426 (2020)

    Article  Google Scholar 

  32. Wang, J.W.: Classification of fingerprint based on traced orientation flow. In: 2010 IEEE International Symposium on Industrial Electronics. IEEE (July 2010)

    Google Scholar 

  33. Wang, J.W., Le, N.T., Wang, C.C., Lee, J.S.: Enhanced ridge structure for improving fingerprint image quality based on a wavelet domain. IEEE Sig. Process. Lett. 22(4), 390–394 (2015)

    Article  Google Scholar 

  34. Watson, C.I., Wilson, C.L.: Nist special database 4. Fingerprint Database Natl. Inst. Stan. Technol. 17(77), 5 (1992)

    Google Scholar 

  35. Win, K.N., Li, K., Chen, J., Viger, P.F., Li, K.: Fingerprint classification and identification algorithms for criminal investigation: a survey. Future Gener. Comput. Syst. 110, 758–771 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Image Processing Laboratory, Signs and Applied Computer (LAPISCO), Fortaleza, Brazil

    João W. Mendes de Souza, Aldisio G. Medeiros, Gabriel B. Holanda & Pedro P. Rebouças Filho

  2. Federal Institute of Ceará (IFCE), Fortaleza, Ceará, Brazil

    Gabriel B. Holanda & Pedro P. Rebouças Filho

  3. Federal University of Cear (UFC), Fortaleza, Ceará, Brazil

    João W. Mendes de Souza, Aldisio G. Medeiros & Paulo A. L. Rego

Authors
  1. João W. Mendes de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aldisio G. Medeiros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel B. Holanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paulo A. L. Rego
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pedro P. Rebouças Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João W. Mendes de Souza .

Editor information

Editors and Affiliations

  1. Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Gregor Rozinaj

  2. Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Radoslav Vargic

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Souza, J.W.M., Medeiros, A.G., Holanda, G.B., Rego, P.A.L., Rebouças Filho, P.P. (2022). Fingerprint Classification Based on the Henry System via ResNet. In: Rozinaj, G., Vargic, R. (eds) Systems, Signals and Image Processing. IWSSIP 2021. Communications in Computer and Information Science, vol 1527. Springer, Cham. https://doi.org/10.1007/978-3-030-96878-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96878-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96877-9

  • Online ISBN: 978-3-030-96878-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation