Learning discriminative and invariant representation for fingerprint retrieval

Science China Information Sciences volume 62, Article number: 19104 (2019) Cite this article

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

References

  1. 1

    Maltoni D, Maio D, Jain A K, et al. Handbook of Fingerprint Recognition. Berlin: Springer, 2003

    Google Scholar 

  2. 2

    Gago-Alonso A, Hernández-Palancar J, Rodríguez-Reina E, et al. Indexing and retrieving in fingerprint databases under structural distortions. Expert Syst Appl, 2013, 40: 2858–2871

    Article  Google Scholar 

  3. 3

    Chen X J, Tian J, Yang X. A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure. IEEE Trans Image Process, 2006, 15: 767–776

    Article  Google Scholar 

  4. 4

    Tang Y, Gao F, Feng J, et al. FingerNet: an unified deep network for fingerprint minutiae extraction. In: Proceedings of IEEE International Joint Conference on Biometrics, 2017. 108–116

    Google Scholar 

  5. 5

    Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2012. 1097–1105

    Google Scholar 

  6. 6

    He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, 2015. 1026–1034

    Google Scholar 

  7. 7

    Wang J, Li Y, Miao Z, et al. Learning deep discriminative features based on cosine loss function. Electron Lett, 2017, 53: 918–920

    Article  Google Scholar 

  8. 8

    Song D, Feng J. Fingerprint indexing based on pyramid deep convolutional feature. In: Proceedings of IEEE International Joint Conference on Biometrics, 2017. 200–207

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61333015).

Author information

Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Dehua Song, Ruilin Li, Fandong Zhang & Jufu Feng

Authors
  1. Dehua Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruilin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fandong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jufu Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dehua Song or Jufu Feng.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Song, D., Li, R., Zhang, F. et al. Learning discriminative and invariant representation for fingerprint retrieval. Sci. China Inf. Sci. 62, 19104 (2019). https://doi.org/10.1007/s11432-018-9512-1

Download citation