Human Lips as Emerging Biometrics Modality

  • Michał Choraś
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)


In many cases human identification biometrics systems are motivated by real-life criminal and forensic applications. One of the most interesting emerging method of human identification, which originates from the criminal and forensic practice, is human lips recognition. In this paper we consider lips’ shape and color features in order to determine human identity. In our lip biometric system geometrical parameters, Zernike moments as well as color features are calculated.


Color Space Face Image Color Feature Zernike Moment Biometric System 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Prabhakar, S., Kittler, J., Maltoni, D., O’Gorman, L., Tan, T.: Introduction to the Special Issue on Biometrics: Progress and Directions. IEEE Trans. on PAMI 29(4), 513–516 (2007)Google Scholar
  2. 2.
    Goudelis, G., Tefas, A., Pitas, I.: On Emerging Biometric Technologies. In: Proc. of COST 275 Biometrics on the Internet, Hatfield, UK, pp. 71–74 (2005)Google Scholar
  3. 3.
    Morales, A., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: A knuckles texture verification method in a transformed domain. In: Proc. of 1st Spanish Workshop on Biometrics (on CD), Girona, Spain (2007)Google Scholar
  4. 4.
    Choraś, M.: Emerging Methods of Biometrics Human Identification. In: Proc. of ICICIC 2007, Kummamoto, Japan. IEEE CS Press, Los Alamitos (2007)Google Scholar
  5. 5.
    Kasprzak, J., Leczynska, B.: Cheiloscopy. Human Identification on the Basis of Lip Prints (in Polish). CLK KGP Press, Warsaw (2001)Google Scholar
  6. 6.
    Kasprzak, J.: Forensic Otoscopy (in Polish). University of Warmia and Mazury Press (2003)Google Scholar
  7. 7.
    Huynh, C., de Chazal, P., Flynn, J., Reilly, R.B.: Automatic Classification of Shoeprints for use in Forensic Science. In: Proc. of the Irish Machine Vision and Image Processing Conference, Dublin, Ireland (2003)Google Scholar
  8. 8.
    Tsuchihasi, Y.: Studies on Personal Identification by Means of Lip Prints. Forensic Science 3(3) (1974)Google Scholar
  9. 9.
    Sonal, V., Nayak, C.D., Pagare, S.S.: Study of Lip-Prints as Aid for Sex Determination. Medico-Legal Update 5(3) (2005)Google Scholar
  10. 10.
    Gomez, E., Travieso, C.M., Briceno, J.C., Ferrer, M.A.: Biometric Identification System by Lip Shape. In: Proc. of Carnahan Conference on Security Technology, pp. 39–42 (2002)Google Scholar
  11. 11.
    Cetingul, H.E., Yemez, Y., Erzin, E., Tekalp, A.M.: Multimodal speaker/speech recognition using lip motion, lip texture and audio. Signal Processing 86, 3549–3558 (2006)CrossRefGoogle Scholar
  12. 12.
    Cetingul, H.E., Yemez, Y., Erzin, E., Tekalp, A.M.: Discriminative Analysis of Lip Motion Features for Speaker Identification and Speech-Reading. IEEE Transactions on Image Processing 15(10), 2879–2891 (2006)CrossRefGoogle Scholar
  13. 13.
    Ouyang, H., Lee, T.: A New Lip Feature Representation Method for Video-based Bimodal Authentication. In: Proc. of NICTA-HCSNet Multimodal User Interaction Workshop, Sydney, Australia, vol. 57, pp. 33–37 (2005)Google Scholar
  14. 14.
    Cetingul, H.E., Yemez, Y., Erzin, E., Tekalp, A.M.: Robust Lip-Motion Features for Speaker Identification. In: Proc. of IEEE Conf. on Acoustics, Speech, and Signal Processing - ICASSP 2005, Philadelphia, USA, pp. 509–512 (2005)Google Scholar
  15. 15.
    Leung, S.H., Wang, S.L., Lau, W.H.: Lip Image Segmentation Using Fuzzy Clustering Incorporating an Elliptic Shape Function. IEEE Trans. Image Processing 13(1), 51–62 (2004)CrossRefGoogle Scholar
  16. 16.
    Kubanek, M.: Method of Speech recogntion and Speaker Identification with Use Audio-Visual of Polish Speech and Hidden Markov Models. In: Saeed, K., et al. (eds.) Biometrics, Computer Security Systems and Artificial Intelligence Applications, pp. 45–55. Springer, NY (2006)CrossRefGoogle Scholar
  17. 17.
    Kubanek, M.: Technique of Video Features Extraction for Audio-video Speach Recognition System. Computing, Multimedia and Intelligent Techniques 1, 181–190 (2005)Google Scholar
  18. 18.
    Tadeusiewicz, R., Korohoda, P.: Computer Analysis and Image Processing (in Polish). Foundation of Progress in Telecommunication, Cracow (1997)Google Scholar
  19. 19.
    Liao, X.S., Pawlak, M.: On the Accuracy of Zernike Moments for Image Analysis. IEEE Trans. Pattern Anal. Machine Intell. 20(12), 1358–1364 (1998)CrossRefGoogle Scholar
  20. 20.
    Xin, Y., Pawlak, M., Liao, S.: Accurate Computation of Zernike Moments in Polar Coordinates. IEEE Trans. Image Processing 16(2), 581–587 (2007)CrossRefGoogle Scholar
  21. 21.
    Teh, C.C., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Machine Intell. 10, 496–513 (1988)MATHCrossRefGoogle Scholar
  22. 22.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Machine Intell. 12, 489–498 (1990)CrossRefGoogle Scholar
  23. 23.
    Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. International Series on Biometrics. Springer, Heidelberg (2006)Google Scholar
  24. 24.
    Erzin, E., Yemez, Y., Tekalp, A.M., Ercil, A., Erdogan, H., Abut, H.: Multimodal Person Recognition for Human-Vehicle Interaction. IEEE Multimedia 13(2), 18–31 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michał Choraś
    • 1
  1. 1.Image Processing Group, Institute of TelecommunicationsUniversity of Technology & Life SciencesBydgoszczPoland

Personalised recommendations