Pattern Analysis and Applications

, Volume 13, Issue 1, pp 105–112 | Cite as

The lip as a biometric

  • Michał ChoraśEmail author
Theoretical Advances


In many cases human identification biometric 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. We present standard and original geometrical parameters used in lips biometric system. Moreover Zernike and Hu moments as well as color features have been used. The presented results are yet not as good as these achieved in other known biometric systems. However, we believe that both lips biometrics as well as our approach and results, are worth to be presented to a wide research community.


Biometrics Human identification Lips recognition Image analysis Pattern recognition 


  1. 1.
    Hurley DJ, Arab-Zavar B, Nixon MS (2007) The ear as a biometric. In: Proceedings of Eusipco’07. Poznan, pp 25–29Google Scholar
  2. 2.
    Choraś M (2007) Image feature extraction methods for ear biometrics—a survey. In: Proceedings of sixth international conference on computer information systems and industrial managementapplications (CISIM’07). IEEE CS Press, Los Alamitos, pp 261–265, June 2007Google Scholar
  3. 3.
    Prabhakar S, Kittler J, Maltoni D, O’Gorman L, Tan T (2007) Introduction to the special issue on biometrics: progress and directions. IEEE Trans PAMI 29(4):513–516Google Scholar
  4. 4.
    Goudelis G, Tefas A, Pitas I (2005) On emerging biometric technologies. In: Proceedings of COST 275 biometrics on the Internet. Hatfield, UK, pp 71–74Google Scholar
  5. 5.
    Morales A, Ferrer MA, Travieso CM, Alonso JB (2007) A knuckles texture verification method in a transformed domain. In: Proceedings of first Spanish workshop on biometrics (on CD), Girona, SpainGoogle Scholar
  6. 6.
    Choraś M (2007) Emerging methods of biometrics human identification. In: Proceedings of ICICIC 2007, Kummamoto, Japan. IEEE CS Press, Los AlamitosGoogle Scholar
  7. 7.
    Yamada M, Kamiya K, Kudo M, Nonaka H, Toyama J (2008) Soft authentication and behavior analysis using a chair with sensors attached: hipprint authentication. Pattern Anal Appl (online first)Google Scholar
  8. 8.
    Hosokawa T, Kudo M, Nonaka H, Toyama J (2008) Soft authentication using an infrared ceiling sensor network. Pattern Anal Appl (online first)Google Scholar
  9. 9.
    Kasprzak J, Leczynska B (2001) Cheiloscopy. Human identification on the basis of lip prints (in Polish). CLK KGP Press, WarsawGoogle Scholar
  10. 10.
    Kasprzak J (2003) Forensic Otoscopy (in Polish). University of Warmia and Mazury Press, OlsztynGoogle Scholar
  11. 11.
    Huynh C, de Chazal P, Flynn J, Reilly RB (2003) Automatic classification of shoeprints for use in forensic science. In: Proceedings of the Irish machine vision and image processing conference, Dublin, IrelandGoogle Scholar
  12. 12.
    Tsuchihasi Y (1974) Studies on personal Identification by means of lip prints. Forensic Sci 3:3Google Scholar
  13. 13.
    Sonal V, Nayak CD, Pagare SS (2005) Study of lip-prints as aid for sex determination, Medico-Legal Update 5(3)Google Scholar
  14. 14.
  15. 15.
    Gomez E, Travieso CM, Briceno JC, Ferrer MA (2002) Biometric Identification system by lip shape. In: Proceedings of Carnahan conference on security technology, pp 39–42Google Scholar
  16. 16.
    Cetingul HE, Yemez Y, Erzin E, Tekalp AM (2006) Multimodal speaker/speech recognition using lip motion, lip texture and audio. Signal Processing 86:3549–3558CrossRefGoogle Scholar
  17. 17.
    Cetingul HE, Yemez Y, Erzin E, Tekalp AM (2006) Discriminative analysis of lip motion features for speaker identification and speech-reading. IEEE Trans Image Process 15(10):2879–2891Google Scholar
  18. 18.
    Ouyang H, Lee T (2005) A new lip feature representation method for video-based bimodal authentication. In: Proceedings of NICTA-HCSNet multimodal user interaction workshop, vol 57. Sydney, Australia, pp 33–37Google Scholar
  19. 19.
    Cetingul HE, Yemez Y, Erzin E, Tekalp AM (2005) Robust lip-motion features for speaker identification. In: Proceedings of IEEE conference on acoustics, speech, and signal processing—ICASSP 2005, Philadelphia, USA, pp 509–512Google Scholar
  20. 20.
    Leung SH, Wang SL, Lau WH (2004) Lip image segmentation using fuzzy clustering incorporating an elliptic shape function. IEEE Trans Image Process 13(1):51–62Google Scholar
  21. 21.
    Nowak H (2006) Lip-reading with discriminative deformable models. Mach Graph Vis 15(3–4):567–576Google Scholar
  22. 22.
    Kubanek M (2006) 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. Springer, New York, pp 45–55CrossRefGoogle Scholar
  23. 23.
    Kubanek M (2005) Technique of video features extraction for audio–video speach recognition system. Comput Multimed Intell Tech 1:181–190Google Scholar
  24. 24.
    Teh CC, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10:496–513zbMATHCrossRefGoogle Scholar
  25. 25.
    Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12:489–498CrossRefGoogle Scholar
  26. 26.
    Choraś M (2007) Human lips recognition. In: Kurzyski M et al (eds) Computer recognition systems, vol 2, Advances in Soft Computing. Springer, Heidelberg, pp 838–843Google Scholar
  27. 27.
    Choraś M (2008) Human lips as emerging biometrics modality. In: Campilho A, Kamel M (eds) Image analysis and recognition, ICIAR 2008, Lecture Notes in Computer Science, vol 5112. Springer, Heidelberg, pp 994–1003Google Scholar
  28. 28.
    Di Gesu V, Valenti C (1995) The discrete symmetry transform in computer vision, Technical Report DMA-011 95, DMA University of PalermoGoogle Scholar
  29. 29.
    Di Gesu V, Valenti C (1995) Symmetry operators in computer vision. In: Proceedings of CCMA workshop on vision modeling and information coding, NiceGoogle Scholar
  30. 30.
    Choraś M, Andrysiak T (2006) Symmetry-based salient points detection in face images. In: Rutkowski L et al (eds) Artificial intelligence and soft computing—ICAISC 2006, Lecture Notes in Computer Science, vol 4029. Springer, Heidelberg, pp 758–767Google Scholar
  31. 31.
    Ross A, Nandakumar K, Jain AK (2006) Handbook of multibiometrics. International Series on Biometrics. Springer, HeidelbergGoogle Scholar
  32. 32.
    Erzin E, Yemez Y, Tekalp AM, Ercil A, Erdogan H, Abut H (2006) Multimodal person recognition for human–vehicle interaction. IEEE Multimed 13:2:18–31CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Image Processing Group, Institute of TelecommunicationsBydgoszczPoland

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