Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10639)

Abstract

A major approach for fingerprint matching today is based on minutiae. However, due to the lack of minutiae, their accuracy degrades significantly for partial-to-partial matching. We propose a novel matching algorithm that makes full use of the distinguishing information in partial fingerprint images. Our model employs the Phase-Only Correlation (POC) function to coarsely assign two fingerprints. Then we use a deep convolutional neural network (CNN) with spatial pyramid pooling to measure the similarity of the overlap areas. Experiments indicate that our algorithm has an excellent performance.

Keywords

Partial fingerprint matching Phase-only correlation Polar Fourier transform Deep convolutional neural networks 

Notes

Acknowledgments

This work was funded by the Chinese National Natural Science Foundation (11331012, 11571014, 11731013).

References

  1. 1.
    Jea, T.Y., Govindaraju, V.: A minutia-based partial fingerprint recognition system. Pattern Recogn. 38(10), 1672–1684 (2005)CrossRefGoogle Scholar
  2. 2.
    Mathur, S., Vjay, A., Shah, J.: Methodology for partial fingerprint enrollment and authentication on mobile devices. In: International Conference on Biometrics, pp. 1–8. IEEE (2016)Google Scholar
  3. 3.
    Sun, Y., Chen, Y., Wang, X.: Deep learning face representation by joint identification verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)Google Scholar
  4. 4.
    Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: Computer Vision and Pattern Recognition, pp. 1875–1882. IEEE (2014)Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  6. 6.
    Koichi, I.T.O., Nakajima, H., Kobayashi, K.: A fingerprint matching algorithm using phase-only correlation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 87(3), 682–691 (2004)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, F., Feng, J.: High-resolution mobile fingerprint matching via deep joint KNN-triplet embedding. In: AAAI, pp. 5019–5020 (2017)Google Scholar
  9. 9.
    Maio, D., Maltoni, D., Cappelli, R.: FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 402–412 (2002)CrossRefGoogle Scholar
  10. 10.
    Maio, D., Maltoni, D., Cappelli, R.: FVC2002: second fingerprint verification competition. In: Proceedings of the 16th International Conference on Pattern recognition, vol. 3, pp. 811–814. IEEE (2002)Google Scholar
  11. 11.
    Maio, D., Maltoni, D., Cappelli, R.: FVC2004: third fingerprint verification competition. In: Biometric Authentication, pp. 31–35 (2004)Google Scholar
  12. 12.
    Jia, X., Yang, X., Zang, Y.: A cross-device matching fingerprint database from multi-type sensors. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3001–3004. IEEE (2012)Google Scholar
  13. 13.
    Roy, A., Memon, N., Ross, A.: MasterPrint: exploring the vulnerability of partial fingerprint-based authentication systems. IEEE Trans. Inf. Forensics Secur. 12(9), 2013–2025 (2017)CrossRefGoogle Scholar
  14. 14.
    Jia, Y., Shelhamer, E., Donahue, J.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  15. 15.
    Alcantarilla, P.F., Solutions, T.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jin Qin
    • 1
  • Siqi Tang
    • 1
  • Congying Han
    • 1
    • 2
    • 3
  • Tiande Guo
    • 1
    • 2
    • 3
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.School of Mathematical ScienceUCASBeijingChina
  3. 3.Key Laboratory of Big Data Mining and Knowledge ManagementUCASBeijingChina

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