International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

A Fully Automatic and Haar like Feature Extraction-Based Method for Lip Contour Detection

    ,

Author Affiliations

  • 1School of Computer Engineering, Shahrood University of Technology, Shahrood, IRAN

Res. J. Recent Sci., Volume 2, Issue (1), Pages 17-20, January,2 (2013)

Abstract

In this paper we propose a fully automatic and efficient method for lip contour detection. At first face region is separated from any background using a variation of AdaBoost classifiers trained with Haarlike features extracted from the face. After that by applying the second classifier trained with mouth Haarlike features, on face region mouth region is extracted, at last sobel edge detection operator applies on mouth region and lip contour is detected. Most of previous methods are based on image color intensity, such methods act very weak on pictures with low contrast and noise, furthermore they are very time consuming. Our method by using Haarlike features is very robust against low contrast and noisy images. It is also very fast, efficient and fully automatic.

References

  1. Yuille A.L., Kohen D.S. and P.W. Hallinan, Feature Extraction from Faces Using Deformable Templates, In Proc. IEEE Intl. Conf. Computer Vision and Pattern Recog.,San Diego, CA, 104–109 (1989)
  2. Luettin J., Thacker N.A. and S.W. Beet., Visual SpeechRecognition Using Active Shape Models and Hidden Markov Models, In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Proc., Atlanta, GA, , 817–820 (1996)
  3. Kass M., Witkin A. and D. Terzopulos, Snakes: Active Contour Models, Intl. J. Computer Vision,1(4), 321–331 (1987)
  4. Eveno N., Caplier A. and Coulon P.Y. New Color Transformation for Lips Segmentation, In Proc. IEEE 4Workshop Multimedia Signal Proc., France, 3–8 (2001)
  5. Wark T., Sridharan S. and Chandran V., An Approach to Statistical Lip Modeling for Speaker Identification via Chromatic Feature Extraction, In Proc. 4th Intl. Conf.Pattern Recog., Brisbane, Australia, 123–125 (1998)
  6. Shamshirband Shahaboddin and Za'fari Ali., Evaluation of the Performance of Intelligent Spray Networks Based On Fuzzy Logic, Res. J. Recent Sci., 1(8), 77-81, August (2012)
  7. Patil Pallavi and Ingle Vikal, Obtaining a high Accurate Fault Classification of Power Transformer based on Dissolved Gas Analysis using ANFIS, Res.J.Recent Sci., 1(2), 97- 99 (2012)
  8. Leung S., Wang S. and Lau W., Lip Image Segmentation Using Fuzzy Clustering Incorporating an Elliptic Shape Function, IEEE Trans. Image Proc., 13(1), 51–62 (2004)
  9. Viola P. and Jones M.J., Robust Real-Time Face Detection, Intl. J. Computer Vision.,57(2), 137–154 (2004)
  10. Freund Y. and Schapire R.E., A Decision-theoretic Generalization of On-line Learning and an Application to Boosting, In Proc. 2nd European Conf. ComputationalLearning Theory, Barcelona, Spain, 23–37 (1995)
  11. Sharifi M. and Shahriari B., Pareto Optimization of Vehicle Suspension Vibration for a Nonlinear Half-car Model Using a Multi-objective Genetic Algorithm, Res.J.Recent Sci., 1(8), 17-22 (2012)
  12. Agbo G.A., Ibeh G.F. and Ekpe J.E., Estimation of Global Solar Radiation at Onitsha with Regression Analysis and Artificial Neural Network Models, Res.J.Recent Sci., 1(6), 27-31 (2012)
  13. Viola P. and Jones M.J., Robust Real-Time Face Detection, Intl. J. Computer Vision.,57(2), 137–154 (2004)
  14. Papageorgiou Oren, and Poggio, A general framework for object detection, International Conference on Computer Vision (1998)
  15. Kanade T., Cohn J.F. and Tian Y., Comprehensive Database for Facial Expression Analysis, In Proc. 4th IEEEIntl. Conf. Automatic Face and Gesture Recog.,France 46–53 (2000)