Advertisement

An improved fingerprint orientation field extraction method based on quality grading scheme

  • Weixin Bian
  • Shifei Ding
  • Yu Xue
Original Article

Abstract

Orientation pattern is an important feature for characterizing fingerprint and plays a very important role in the automatic fingerprint identification system (AFIS). Conventional gradient based methods are popular but very sensitive to noise. In this paper, we present an improved fingerprint orientation field (FOF) extraction method based on quality grading scheme. In order to effectively remove the noise, the point orientations are fitted by using 2D discrete orthogonal polynomial. The role of the gradient modulus is taken into full account, and the weights of the point orientations are obtained by computing the similarity of the fitted point orientations. The block qualities are assessed by the coherence of point orientations and the block orientations are estimated based on quality grading scheme. In the proposed method, it does not need any prior knowledge of singular points. To validate the performance, the proposed method has been applied to fingerprint singularity detection and fingerprint recognition. We compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. Our statistical experiments show that the proposed method can significantly improve in both singular point detection and matching rates, and it is more robust against noise.

Keywords

Orientation field 2D discrete orthogonal polynomials Gradient based weighted averaging Quality grading 

Notes

Acknowledgements

The work is partially supported by the National Natural Science Foundation of China under Grant No. 61672522, Anhui Provincial Natural Science Foundation under Grant No. 1708085MF145, the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD), and the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET).

References

  1. 1.
    Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406CrossRefGoogle Scholar
  2. 2.
    Xia Z, Wang X, Zhang L et al (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Security. doi: 10.1109/TIFS.2016.2590944 Google Scholar
  3. 3.
    Zhou Z, Wang Y, Jonathan Q et al (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Security. doi: 10.1109/TIFS.2016.2601065 Google Scholar
  4. 4.
    Wei X, Wang H, Guo G et al (2015) Multiplex image representation for enhanced recognition. Int J Mach Learn Cyber. doi: 10.1007/s13042-015-0427-5 Google Scholar
  5. 5.
    Maltoni D, Maio D, Jain A, et al. (2009) Handbook of fingerprint recognition, 2nd edn. Springer-Verlag, London, UKCrossRefMATHGoogle Scholar
  6. 6.
    Gottschlich C (2012) Curved-region-based ridge frequency estimation and curved gabor filters for fingerprint image enhancement. IEEE Trans Image Process 21(4):2220–2227MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Sutthiwichaiporn P, Areekul V (2013) Adaptive boosted spectral filtering for progressive fingerprint enhancement. Pattern Recogn 46(9):2465–2486CrossRefGoogle Scholar
  8. 8.
    Karu K, Jain A (1996) Fingerprint classification. Pattern Recognit 29(3):389–404CrossRefGoogle Scholar
  9. 9.
    Bazen A, Gerez S (2002) Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans Pattern Anal Mach Intell 24(7):905–919CrossRefGoogle Scholar
  10. 10.
    Zhou J, Chen F, Gu J (2009) A novel algorithm for detecting singular points from fingerprint images. IEEE Trans Pattern Anal Mach Intell 31(7):1239–1250CrossRefGoogle Scholar
  11. 11.
    Liu M (2010) Fingerprint classification based on Adaboost learning from singularity features. Pattern Recognit 43(3):1062–1070CrossRefMATHGoogle Scholar
  12. 12.
    He Y, Tian J, Li L et al (2006) Fingerprint matching based on global comprehensive similarity. IEEE Trans Pattern Anal Mach Intell 28(6):169–177Google Scholar
  13. 13.
    Feng J (2008) Combining minutiae descriptors for fingerprint matching. Pattern Recognit 41(1):342–352CrossRefMATHGoogle Scholar
  14. 14.
    Cao K, Yang X, Chen X (2012) A novel ant colony optimization algorithm for large-distorted fingerprint matching. Pattern Recognit 45(1):151–161CrossRefGoogle Scholar
  15. 15.
    Kass M, Witkin A (1987) Analyzing oriented patterns. Comput Vis Graph Image Process 37(3):362–385CrossRefGoogle Scholar
  16. 16.
    Rao A, Jain R (1992) Computerized flow field analysis: oriented texture fields. IEEE Trans Pattern Anal Machine Intell 14(7):693–709CrossRefGoogle Scholar
  17. 17.
    Jiang X (2005) On orientation and anisotropy estimation for online fingerprint authentication. IEEE Trans Signal Process 53(10):4038–4049MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Wang Y, Hu J, Han F (2007) Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Appl Math Comput 185(2):823–833MATHGoogle Scholar
  19. 19.
    Jiang X (2007) Extracting image orientation feature by using integration operator. Pattern Recognit 40(2):705–717MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Mei Y, Cao G, Sun H et al (2012) A systematic gradient-based method for the computation of fingerprint’s orientation field. Comput Electr Eng 38:1035–1046CrossRefGoogle Scholar
  21. 21.
    Sherlock B, Monro D (1993) A model for interpreting fingerprint topology. Pattern Recognit 26:1047–1055CrossRefGoogle Scholar
  22. 22.
    Li J, Yau W, Wang H (2006) Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recognit 39(1):102–114CrossRefGoogle Scholar
  23. 23.
    Ram S, Bischof H, Brichbauer J (2010) Modelling fingerprint ridge orientation using Legendre polynomials. Pattern Recognit 43(1):342–357CrossRefMATHGoogle Scholar
  24. 24.
    Jirachaweng S, Hou Z, Yau W et al (2011) Residual orientation modeling for fingerprint enhancement and singular point detection. Pattern Recognit 44(2):431–442CrossRefMATHGoogle Scholar
  25. 25.
    Bian W, Luo Y, Xu D et al (2014) Fingerprint ridge orientation field estimation using the best quadratic approximation by orthogonal polynomials in two discrete variables. Pattern Recognit 47(10):3304–3313CrossRefGoogle Scholar
  26. 26.
    Perona P (1998) Orientation diffusions. IEEE Trans Image Process 7(3):457–467CrossRefGoogle Scholar
  27. 27.
    Liu M, Liu S, Zhao Q (2014) Fingerprint orientation field estimation by weighted discrete cosine transform. Inf Sci 268:65–77CrossRefGoogle Scholar
  28. 28.
    Karu K, Jain AK (1996) Fingerprint classification. Pattern Recognit 29(3):389–404CrossRefGoogle Scholar
  29. 29.
    Tico M, Kuosmanen P (2003) Fingerprint matching using an orientation-based minutia descriptor. IEEE Trans Pattern Anal Machine Intell 25(8):1009–1014CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.School of Mathematics and Computer ScienceAnhui Normal UniversityWuhuChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations