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On a Lip Print Recognition by the Pattern Kernel with Multi-resolution Architecture

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Intelligent Data Engineering and Automated Learning (IDEAL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2690))

Abstract

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify a person. They have sensors to pick up some physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However lip-print recognition has been less developed than recognition of other human physical attributes such as the fingerprint, voice patterns, retinal blood vessel patterns, or the face. The lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from the pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. The multi-resolution architecture allows us to reduce the false recognition rate from 15% to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.

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References

  1. Wark, T., Sridharan, S., Chandran, V.: The use of speech and lip modalities for robust speaker verification under adverse conditions. In: IEEE MCS 1998. IEEE, Los Alamitos (1998)

    Google Scholar 

  2. Goudail, F., Lange, E., Iwamoto, T., Kyuma, K., Otsu, N.: Face recognition system using local autocorrelations and multiscale integration. IEEE Trans. Pattern Analysis and Machine Intelligence 18, 1024–1028 (1996)

    Article  Google Scholar 

  3. Lades, M., Vorbruggen, C., Buhmann, J., Lange, J., Wurtz, C.M.R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Computers 42, 300–311 (1993)

    Article  Google Scholar 

  4. Brunelli, R., Poggio, T.: Face recognition: Features versus templates. IEEE Trans. Pattern Analysis and Machine Intelligence 15, 1042–1052 (1993)

    Article  Google Scholar 

  5. Lievin, M., Delmas, P., Coulon, P.Y., Luthon, F., Fristot, V.: Automatic lip tracking: Bayesian segmentation and active contours in a cooperative scheme. In: IEEE MCS 1998. IEEE, Los Alamitos (1998)

    Google Scholar 

  6. Oliver, N., Pentland, A.: Lafter: Lips and face real-time tracker. In: EMMCVPR 1997. IEEE, Los Alamitos (1997)

    Google Scholar 

  7. Samal, A., Iyengar, P.A.: Automatic recognition and analysis of human faces and facial expressions: A survey. IEEE Trans. Pattern Recognition 25, 65–67 (1992)

    Article  Google Scholar 

  8. Valentin, D., Abdi, H., O’Toole, A.J., Cottrell, G.W.: Connectionist models of face processing: A survey. IEEE Trans. Pattern Recognition 27, 1209–1230 (1994)

    Article  Google Scholar 

  9. Pratt, W.K.: Digital Image Processing, 3rd edn. John Wiley, Chichester (2001)

    Book  Google Scholar 

  10. Ashbourn, J.D.M.: Biometrics: Advanced Identify Verification: The Complete Guide. Springer, New York (2000)

    Google Scholar 

  11. Lucey, S., Sridharan, S., Chandran, V.: Initialized eigenlip estimator for fast lip tracking using linear regression. In: IEEE ICPR 2000. IEEE, Los Alamitos (2000)

    Google Scholar 

  12. Zhang, D.D.: Automated Biometrics: Technologies and Systems. Kluwer Academic Publishers, Boston (2000)

    Google Scholar 

  13. Jain, A., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers, Boston (1999)

    Google Scholar 

  14. Abe, S.: Pattern Classification. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  15. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley- Interscience, Chichester (2000)

    Google Scholar 

  16. Javidi, B.: Image Recognition and Classification. Marcel Dekker, New York (2002)

    Book  MATH  Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Kim, J.O., Baik, K.S., Chung, C.H. (2003). On a Lip Print Recognition by the Pattern Kernel with Multi-resolution Architecture. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_77

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  • DOI: https://doi.org/10.1007/978-3-540-45080-1_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40550-4

  • Online ISBN: 978-3-540-45080-1

  • eBook Packages: Springer Book Archive

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