Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Research on partial fingerprint recognition algorithm based on deep learning

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Fingerprint recognition technology is widely used as a kind of powerful and effective authentication method on various mobile devices. However, most mobile devices use small-area fingerprint scanners, and these fingerprint scanners can only obtain a part of the user’s fingerprint information. Besides, traditional fingerprint recognition algorithms excessively rely on the details of fingerprints, and their recognition performance has great limitations in mobile devices which can only get partial fingerprint images due to fingerprint scanners. This paper proposes a partial fingerprint recognition algorithm based on deep learning for the recognition of partial fingerprint images. It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction and finally improve the recognition performance of partial fingerprint images. The experimental results show that the fingerprint recognition algorithm in this paper has a better performance than the existing fingerprint recognition algorithm based on deep learning on the problem of partial fingerprint classification and fingerprint recognition in the public dataset NIST-DB4 and self-built dataset NCUT-FR.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

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

References

  1. Lin X, Huang X, Xiaosheng SU et al (2006) Progress of biometric technology standardization. J Tsinghua Univ 46(2):194–198

    Google Scholar 

  2. Shalaby MAW, Ahmad MO (2013) A multilevel structural technique for fingerprint representation and matching. Sig Process 93(1):56–69

    Article  Google Scholar 

  3. Sun DM, Qiu ZD (2001) Survey of the emerging biometric technology. Acta Electron Sin 2:213–218

    Google Scholar 

  4. Monika KM (2015) A novel fingerprint minutiae matching using LBP. In: International conference on reliability, INFOCOM technologies and optimization. IEEE, pp 1–4

  5. Mitchell MR, Link RE, Masmoudi AD et al (2010) Implementation of a fingerprint recognition system using LBP descriptor. J Test Eval 38(3):369–382

    Google Scholar 

  6. Syarif MA, Ong TS, Tee C (2014) Fingerprint recognition based on multi-resolution histogram of gradient descriptors. In: The 8th international conference on robotic, vision, signal processing and power applications. Springer, Singapore, pp 189–196

    Google Scholar 

  7. Gottschlich C, Marasco E, Yang AY et al (2014) Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE international joint conference on biometrics. IEEE, pp 1–7

  8. Zhong Y, Peng X (2015) SIFT-based low-quality fingerprint LSH retrieval and recognition method. Int J Signal Process Image Process Pattern Recognit 8:263–272

    Google Scholar 

  9. Park U, Pankanti S, Jain AK (2008) Fingerprint verification using SIFT features. In: SPIE defense and security symposium. international society for optics and photonics, pp 69440K–69440K-9

  10. Awad AI, Baba K (2012) Evaluation of a fingerprint identification algorithm with SIFT features. In: Iiai international conference on advanced applied informatics. IEEE Computer Society, pp 129–132

  11. Lathajothi V, Arumugam S (2013) High-resolution fingerprint matching using level 3 incipient ridges and scars. Int J Comput Appl 48(8):19–22

    Google Scholar 

  12. Jain AK, Feng J (2011) Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell 33(1):88

    Article  Google Scholar 

  13. Chen Fanglin, Li Ming, Zhang Yi (2013) A fusion method for partial fingerprint recognition. Int J Pattern Recognit Artif Intelligence 27(06):121–65390D9

    Article  Google Scholar 

  14. Fernandez-Saavedra B, Sanchez-Reillo R, Ros-Gomez R et al (2016) Small fingerprint scanners used in mobile devices: the impact on biometric performance. Iet Biom 5(1):28–36

    Article  Google Scholar 

  15. Lee W, Cho S, Choi H et al (2017) Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners. Expert Syst Appl 87:183–198

    Article  Google Scholar 

  16. Miron R, Letia T (2010) Fuzzy logic decision in partial fingerprint recognition. In: IEEE Computer Society

  17. Zanganeh O, Srinivasan B, Bhattacharjee N (2014) Partial fingerprint matching through region-based similarity. In: International conference on digital image computing: techniques and applications. IEEE, pp 1–8

  18. Wang Y, Hu J (2011) Global Ridge orientation modeling for partial fingerprint identification. IEEE Trans Pattern Anal Mach Intell 33(1):72–87

    Article  Google Scholar 

  19. Zhang S, Gong Y, Wang J (2017) The development of deep convolution neural network and its applications on computer vision, vol 40, Online Publishing No. 144

  20. Kuang H, Liu C, Chan LLH et al (2018) Multi-class fruit detection based on image region selection and improved object proposals. Neurocomputing 283:241–255

    Article  Google Scholar 

  21. Sun Q, Wang Q, Zhang J et al (2017) Hyperlayer Bilinear Pooling with application to fine-grained categorization and image retrieval. Neurocomputing 282:174–183

    Article  Google Scholar 

  22. Geng Q, Zhou Z, Cao X (2018) Survey of recent progress in semantic image segmentation with CNNs. Sci China Inf Sci 61(5):051101

    Article  MathSciNet  Google Scholar 

  23. Han D, Liu Q, Fan W (2018) A new image classification method using CNN transfer learning and web data augmentation. Expert Syst Appl 95:43–56

    Article  Google Scholar 

  24. Zhang F, Feng J High-resolution mobile fingerprint matching via deep joint KNN-triplet embedding. In: Proceedings of the thirty-first AAAI conference on artificial intelligence (AAAI-17)

  25. Zhang Y, Zhou B, Zan X (2017) Small-size fingerprint matching based on deep learning. J Comput Appl 37(11):3212–3218

    Google Scholar 

  26. Zhendong WU, Wang Y, Zhang J et al (2017) Fouling and damaged fingerprint recognition based on deep learning. J Electron Inf Technol 39(7):1585–1591

    Google Scholar 

  27. Espiritu JD, Rolluqui G, Gustilo RC (2016) Neural network based partial fingerprint recognition as support for forensics. In: international conference on humanoid, nanotechnology, information technology, communication and control, environment and management. IEEE, pp 1–5

  28. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition. IEEE, Las Vegas, pp 770–778

    Google Scholar 

  29. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. European conference on computer vision. Springer, Cham, pp 630–645

    Chapter  Google Scholar 

  30. Zagoruyko S, Komodakis N (2016) Wide residual networks. British machine vision conference, pp 87.1–87.12

  31. Li Q, Sun Z, He R, Tan T (2017) Deep supervised discrete hashing. arXiv:1705.10999

  32. Liu H, Wang R, Shan S et al (2016) Deep supervised hashing for fast image retrieval. In: Computer vision and pattern recognition. IEEE, pp 2064–2072

  33. Watson CI (1992) NIST special database 4, fingerprint database. National Institute of Standards & Technology

  34. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal Covariate shift, pp 448–456

  35. Peralta D, Triguero I, García S et al (2018) On the use of convolutional neural networks for robust classification of multiple fingerprint captures. Int J Intell Syst 33(1):213–230

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. North China University of Technology, Beijing, China

    Fanfeng Zeng, Shengda Hu & Ke Xiao

Authors
  1. Fanfeng Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengda Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ke Xiao.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Funding

This work is supported by the National Key Research and Development Program under Grant 2017YFB0802300.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, F., Hu, S. & Xiao, K. Research on partial fingerprint recognition algorithm based on deep learning. Neural Comput & Applic 31, 4789–4798 (2019). https://doi.org/10.1007/s00521-018-3609-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3609-8

Keywords

Search

Navigation