On a Lip Print Recognition by the Pattern Kernel with Multi-resolution Architecture
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.
KeywordsInput Image Recognition Rate Recognition System Biometric System False Acceptance Rate
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 15.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley- Interscience, Chichester (2000)Google Scholar