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A CNN-Based Fingerprint Image Quality Assessment Method

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Biometric Recognition (CCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

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Abstract

Fingerprint image quality assessment is a very important task as the performance of automatic fingerprint identification systems relies heavily on the quality of fingerprint images. Existing methods have made many efforts to find out more appropriate solutions, but most of them operate either on full regions of a fingerprint image, or on local areas. Unlike previous methods, we divide fingerprint images into blocks, and define the quality levels of the blocks according to the minutiae on them and their ridge orientation certainty. With the manually prepared quality-specific fingerprint image blocks, we train a convolutional neural network (CNN) to fulfill end-to-end quality prediction for fingerprint image blocks. The global quality of a fingerprint image can be obtained by fusing the quality levels of its blocks. We evaluate the proposed method on FVC2002 DB1A and FVC2002 DB2A. Experimental results show that the proposed method can effectively distinguish good quality fingerprints from bad ones, and ensure high fingerprint recognition accuracy.

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References

  1. Ratha, N.K., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2004)

    Book  Google Scholar 

  2. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, London (2009)

    Book  MATH  Google Scholar 

  3. Yao, Z.G., Bars, J.M.L., Charrier, C., Rosenberger, C.: A literature review of fingerprint quality assessment and its evaluation. IET Biometrics 5(3), 243–251 (2016)

    Article  Google Scholar 

  4. Yao, Z., Bars, J.L., Charrier, C., Rosenberger, C.: Quality assessment of fingerprints with minutiae delaunay triangulation. In: 2015 International Conference on Information Systems Security and Privacy, pp. 315–321 (2015)

    Google Scholar 

  5. Lee, B., Moon, J.: A novel measure of fingerprint image quality using the Fourier spectrum. In: Proceedings of SPIE - The International Society for Optical Engineering, pp. 105–112 (2005)

    Google Scholar 

  6. Bazen, A.M., Gerez, S.H.: Segmentation of fingerprint images. In: ProRISC 2001 Workshop on Circuits, Systems and Signal Processing, pp. 276–280 (2001)

    Google Scholar 

  7. Shen, L.L., Kot, A., Koo, W.M.: Quality measures of fingerprint images. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 266–271. Springer, Heidelberg (2001). doi:10.1007/3-540-45344-X_39

    Chapter  Google Scholar 

  8. Ballan, M., Sakarya, F.A., Evans, B.L.: A fingerprint classification technique using directional images. In: Thirty-First Asilomar Conference on Signals, Systems and Computers, pp. 101–104 (1997)

    Google Scholar 

  9. Chen, T.P., Jiang, X., Yau, W.Y.: Fingerprint image quality analysis. In: International Conference on Image Processing, pp. 1253–1256 (2004)

    Google Scholar 

  10. Xie, S.J., Yang, J.C., Yoon, S., Dong, S.P.: An optimal orientation certainty level approach for fingerprint quality estimation. In: Second International Symposium on Intelligent Information Technology Application, pp. 722–726 (2008)

    Google Scholar 

  11. Tabassi, E., Wilson, C., Watson, C.: NIST fingerprint image quality. NIST Res. Rep. NIST7151 (2004)

    Google Scholar 

  12. Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. In: IEEE International Conference on Image Processing, pp. 37–40 (2005)

    Google Scholar 

  13. Olsen, M.A., Tabassi, E., Makarov, A., Busch, C.: Self-organizing maps for fingerprint image quality assessment. In: Computer Vision and Pattern Recognition Workshops, pp. 138–145 (2013)

    Google Scholar 

  14. Yao, Z., Bars, J.M.L., Charrier, C., Rosenberger, C.: Fingerprint quality assessment combining blind image quality, texture and minutiae features. In: International Conference on Information Systems Security and Privacy (2015)

    Google Scholar 

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

    Article  Google Scholar 

  16. Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn. 28(11), 1657–1672 (1995)

    Article  Google Scholar 

  17. Neurotechnology Inc.: VeriFinger. http://www.neurotechnology.com/

  18. FVC2002 database. http://bias.csr.unibo.it/fvc2002/

  19. FVC2004 database. http://bias.csr.unibo.it/fvc2004/

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61202161, 61403257) and Shenzhen Fundamental Research Funds (No. JCYJ20150324140036868).

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Authors and Affiliations

  1. National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China

    Jianqi Yan, Xiaowei Dai & Qijun Zhao

  2. School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Feng Liu

Authors
  1. Jianqi Yan
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  2. Xiaowei Dai
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  3. Qijun Zhao
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  4. Feng Liu
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Corresponding author

Correspondence to Qijun Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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Yan, J., Dai, X., Zhao, Q., Liu, F. (2017). A CNN-Based Fingerprint Image Quality Assessment Method. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

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