Advertisement

Fingerprint Image Segmentation Using Textural Features

  • Reji C. JoyEmail author
  • M. Azath
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Automatic Fingerprint Identification System (AFIS) uses fingerprint segmentation as its pre-processing step. A fingerprint segmentation step divides the fingerprint image into foreground and background. An AFIS that uses a feature extraction algorithm for person identification will tend to fail if it extracts spurious features from the noisy background area. So fingerprint image segmentation plays a crucial role in reliably separating ridge like part (foreground) from its background. In this paper, an algorithm for fingerprint image segmentation using GLCM textural feature is presented. Four block level GLCM features: Contrast, Correlation, Energy and Homogeneity are used for fingerprint segmentation. A linear classifier is trained for classifying per block of fingerprint image. The algorithm is tested on standard FVC2002 dataset. Experimental results show that the proposed segmentation method works well in noisy fingerprint images.

Keywords

Fingerprint Segmentation GLCM features Logistic regression Morphological operations 

References

  1. 1.
    Bazen, A. M., Gerez, S. H.: Segmentation of fingerprint images. In: ProRISC 2001 Workshop on Circuits, Systems and Signal Processing, (2001)Google Scholar
  2. 2.
    Alonso-Fernandez, F., Fierrez-Aguilar, J., Ortega-Garcia, J.: An enhanced Gabor filter-based segmentation algorithm for fingerprint recognition systems. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA 2005), pp. 239–244, (2005)Google Scholar
  3. 3.
    Chen, X., Tian, J., Cheng, J., Yang, X.: Segmentation of fingerprint images using linear classifier. EURASIP Journal on Applied Signal Processing. 2004(4), pp. 480–494, (2004)CrossRefGoogle Scholar
  4. 4.
    Wu, C., Tulyakov, S., Govindaraju, V.: Robust Point-Based Feature Fingerprint Segmentation Algorithm. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007, LNCS, vol. 4642, pp. 1095–1103, Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Jain, A. K., Ross, A.: Fingerprint Matching Using Minutiae and Texture Features. In: Proceeding of International Conference on Image Processing, pp. 282–285, (2001)Google Scholar
  6. 6.
    Haralick, R. M., Shanmugan, K., Dinstein, J.: Textual features for image classification. IEEE Trans. Syst. Man. Cybern. Vol. SMC-3, pp. 610–621, (1973)Google Scholar
  7. 7.
    Haralick, R. M.: Statistical and Structural Approaches to Texture. In: Proceedings of IEEE, Vol. 67, No. 5, pp. 768–804, (1979)CrossRefGoogle Scholar
  8. 8.
    Zacharias, G.C., Lal, P.S.: Combining Singular Point and Co-occurrence Matrix for Fingerprint Classification. In: Proceedings of the Third Annual ACM Bangalore Conference, pp. 1–6, (2010)Google Scholar
  9. 9.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition (Second Edition). Springer, New York (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition. Vol. 26, No. 9, pp. 1277–1294, (1993)CrossRefGoogle Scholar
  11. 11.
    Gonzalez, R. C., Wintz, P.: Digital Image Processing.2nd Edition. Addison-Wesley, (1987)Google Scholar
  12. 12.
    Soille, P., Morphological Image Analysis: Principles and Applications. Springer-Verlag, pp. 173–174, (1999)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Karpagam UniversityCoimbatoreIndia
  2. 2.Karpagam UniversityCoimbatoreIndia

Personalised recommendations