Advertisement

Effective Lip Prints Preprocessing and Matching Methods

  • Krzysztof WrobelEmail author
  • Piotr Porwik
  • Rafal Doroz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

This paper presents a method of recognition of human lips. It can be treated as a new kind of biometric measure. During image preprocessing, the features are extracted from the lip print image. In the same step, image is denoised and normalized and ROI is determined. In the next stage, the normalized cross-correlation method was applied to estimation of the biometric parameters EER, FAR, and FRR. Investigations were conducted on 120 lip print images. These images come from University of Silesia public repository http://biometrics.us.edu.pl. The results obtained are very promising and suggest that the proposed recognition method can be introduced into professional forensic identification systems.

Keywords

Biometrics Lip print Image preprocessing Normalized cross-correlation 

References

  1. 1.
    Briechle, K., Hanebeck, U.D.: Template matching using fast normalized cross-correlation. In: Proceedings of SPIE, Aero-Sense Symposium, vol. 4387. Orlando, Florida (2001)Google Scholar
  2. 2.
    Dougherty, E.: An Introduction to Morphological Image Processing (1992)Google Scholar
  3. 3.
    Johnson, A.Y., Sun, J., Bobick, A.F.: Using similarity scores from a small gallery to estimate recognition performance for larger galleries. In: IEEE International Workshop on analysis and modeling of faces and gestures, AMFG2003, pp. 100–103 (2003)Google Scholar
  4. 4.
    Kasprzak, J., Leczynska, B.: Cheiloscopy. Human Identification on the Basis of a Lip Trace (in Polish) (2001)Google Scholar
  5. 5.
    Kudlacik, P., Porwik, P.: A new approach to signature recognition using the fuzzy method. Pattern Anal. Appl. 17(3), 451–463 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Porwik, P., Doroz, R., Orczyk, T.: The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition. Pattern Anal. Appl. 1–19. doi: 10.1007/s10044-014-0419-1
  7. 7.
    Porwik, P., Orczyk, T.: DTW and voting-based lip print recognition system. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds.) Computer Information Systems and Industrial Management. LNCS, vol. 7564, pp. 191–202. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. SMC-8, 630–632 (1978)Google Scholar
  9. 9.
    Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognit. 33, 225–236 (2000)CrossRefGoogle Scholar
  10. 10.
    Siegel, J., et al.: Encyclopedia of Forensic Science, pp. 358–362 (2000)Google Scholar
  11. 11.
    Utsuno, H., et al.: Preliminary study of post mortem identification using lip prints. Forensic Sci. Int. 149(23), 129–132 (2005)CrossRefGoogle Scholar
  12. 12.
    Wrobel, K., Doroz, R., Palys, M.: A method of lip print recognition based on sections comparison. In: Proceedings of 2013 IEEE International Conference on Biometrics and Kansei Engineering (ICBAKE 2013), pp. 47–52. Tokyo, Japan (2013)Google Scholar
  13. 13.
    Wrobel, K., Doroz, R., Palys, M.: Lip print recognition method using bifurcations analysis. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) Intelligent Information and Database Systems. LNCS, vol. 9012, pp. 72–81. Springer, Heidelberg (2015) Google Scholar
  14. 14.
    Yang, H., Kot, A.C., Jiang, X.: Binarization of low-quality barcode images captured by mobile phones using local window of adaptive location and size. IEEE Trans. Image Process. 21(1), 418–425 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer Science, University of SilesiaSosnowiecPoland

Personalised recommendations