Advertisement

Local Contrast Phase Descriptor for Quality Assessment of Fingerprint Images

  • Ram Prakash SharmaEmail author
  • Somnath Dey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)

Abstract

Fingerprint image quality is one of the main factors affecting the recognition performance of Automatic Fingerprint Identification System (AFIS). Therefore, analysis of fingerprint image quality is an important task during the acquisition. In this work, local contrast phase descriptor (LCPD) is used to analyze the texture quality of fingerprint images. Spatial and transform domain features computed using LCPD are fed to Support Vector Machine (SVM) classifier for fingerprint texture classification in wet, dry, and good class. Experimental evaluations performed on low-quality FVC 2004 DB1 dataset outperforms the current state-of-the-art methods. Therefore, utilizing the proposed method for quality control of fingerprint images during acquisition can help in improving the performance of fingerprint recognition system.

Keywords

Biometrics Fingerprint quality Local texture descriptor Support vector machine 

Notes

Acknowledgment

This research work has been carried out with the financial support provided from Science and Engineering Research Board (SERB), DST (ECR/2017/000027), Govt. of India.

References

  1. 1.
    Alonso-Fernandez, F., et al.: A comparative study of fingerprint image-quality estimation methods. IEEE Trans. Inf. Forensic Secur. 2(4), 734–743 (2007)CrossRefGoogle Scholar
  2. 2.
    Awasthi, A., Venkataramani, K., Nandini, A.: Image quality quantification for fingerprints using quality-impairment assessment. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 296–302 (2013)Google Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  4. 4.
    Chen, J., Shan, S., Zhao, G., Chen, X., Gao, W., Pietikainen, M.: A robust descriptor based on Webers law. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)Google Scholar
  5. 5.
    Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: Local contrast phase descriptor for fingerprint liveness detection. Pattern Recogn. 48(4), 1050–1058 (2015)CrossRefGoogle Scholar
  6. 6.
    Lim, E., Toh, K.A., Suganthan, P.N., Jiang, X., Yau, W.Y.: Fingerprint image quality analysis. In: International Conference on Image Processing (ICIP), vol. 2, pp. 1241–1244 (2004)Google Scholar
  7. 7.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2004: third fingerprint verification competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25948-0_1CrossRefGoogle Scholar
  8. 8.
    Mehtre, B.M.: Fingerprint image analysis for automatic identification. Mach. Vis. Appl. 6(2), 124–139 (1993)CrossRefGoogle Scholar
  9. 9.
    Munir, M.U., Javed, M.Y., Khan, S.A.: A hierarchical k-means clustering based fingerprint quality classification. Neurocomputing 85, 62–67 (2012)CrossRefGoogle Scholar
  10. 10.
    Ojansivu, V., Rahtu, E., Heikkila, J.: Rotation invariant local phase quantization for blur insensitive texture analysis. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  11. 11.
    Olsen, M.A., Smida, V., Busch, C.: Finger image quality assessment features: definitions and evaluation. IET Biometrics 5(2), 47–64 (2016)CrossRefGoogle Scholar
  12. 12.
    Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, pp. 582–588 (1999)Google Scholar
  13. 13.
    Sharma, R.P., Dey, S.: Fingerprint image quality assessment and scoring. In: Ghosh, A., Pal, R., Prasath, R. (eds.) MIKE 2017. LNCS (LNAI), vol. 10682, pp. 156–167. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71928-3_16CrossRefGoogle Scholar
  14. 14.
    Sharma, R.P., Dey, S.: Two-stage quality adaptive fingerprint image enhancement using fuzzy c-means clustering based fingerprint quality analysis. Image Vis. Comput. 83–84, 1–16 (2019).  https://doi.org/10.1016/j.imavis.2019.02.006CrossRefGoogle Scholar
  15. 15.
    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).  https://doi.org/10.1007/3-540-45344-X_39CrossRefGoogle Scholar
  16. 16.
    Tabassi, E.: Development of NFIQ 2.0. NIST (2015). https://www.nist.gov/services-resources/software/development-nfiq-20
  17. 17.
    Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. In: International Conference on Image Processing, vol. 2, pp. 37–40. IEEE (2005)Google Scholar
  18. 18.
    Tertychnyi, P., Ozcinar, C., Anbarjafari, G.: Low-quality fingerprint classification using deep neural network. IET Biometrics 7(6), 550–556 (2018)CrossRefGoogle Scholar
  19. 19.
    Wu, C., Chiu, C.: Dry fingerprint detection for multiple image resolutions using ridge features. In: IEEE International Workshop on Signal Processing Systems (SiPS), pp. 1–5, October 2017Google Scholar
  20. 20.
    Yang, X.K., Luo, Y.: A classification method of fingerprint quality based on neural network. In: International Conference on Multimedia Technology, pp. 20–23 (2011)Google Scholar
  21. 21.
    Yao, Z., bars, J.L., Charrier, C., Rosenberger, C.: Quality assessment of fingerprints with minutiae delaunay triangulation. In: International Conference on Information Systems Security and Privacy (ICISSP), pp. 315–321 (2015)Google Scholar
  22. 22.
    Yao, Z., Bars, J.M.L., Charrier, C., Rosenberger, C.: Literature review of fingerprint quality assessment and its evaluation. IET Biometrics 5(3), 243–251 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology IndoreIndoreIndia

Personalised recommendations