An Adept Segmentation Algorithm and Its Application to the Extraction of Local Regions Containing Fiducial Points

  • Erhan AliRiza İnce
  • Syed Amjad Ali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


Locating human fiducial points like eyes and mouth in a frontal head and shoulder image is an active research area for applications such as model based teleconferencing systems, model based low bit rate video transmission, computer based identification and recognition systems. This paper proposes an adept and efficient rule based skin color region extraction algorithm using normalized r-g color space. The given scheme extracts the skin pixels employing a simple quadratic polynomial model and some additional color based rules to extract possible eye and lip regions. The algorithm refines the search for fiducial points by eliminating falsely extracted feature components using spatial and geometrical representations of facial components. The algorithm described herein has been implemented and tested with 311 images from FERET database with varying light conditions, skin colors, orientation and tilts. Experimental results indicate that the proposed algorithm is quite robust and leads to good facial feature extraction.


Binary Mask Mouth Region Fiducial Point Facial Component Face Recognition Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Rein-Lien, H., Abdel-Mottaleb, M.: Face detection in colour images. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)CrossRefGoogle Scholar
  2. 2.
    Ming-Hsuan, Y., Kriegnam, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  3. 3.
    Alattar, A.M., Rajala, S.A.: Facial Features Localization In Front View Head And Shoulders Images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 1999, vol. 6, pp. 3557–3560 (1999)Google Scholar
  4. 4.
    Rein-Lien, H., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. Tech. Report MSU-CSE-01-7, Michigan State University (March 2001)Google Scholar
  5. 5.
    Ínce, E.A., Kaymak, S., Çelik, T.: Yüzsel Öznitelik Sezimi Için Karma Bir Teknik. In: 13. IEEE Sinyal Ísleme ve Íletisim Uygulamaları Kurultayı, May 2005, pp. 396–399 (2005)Google Scholar
  6. 6.
    Hu, M., Worrall, S., Sadka, A.H., Kondoz, A.M.: Face Feature Detection And Model Design For 2D Scalable Model-Based Video Coding. In: International Conference on Visual Information Engineering, July 2003, pp. 125–128 (2003)Google Scholar
  7. 7.
    Sung, K., Poggio, T.: Example based Learning for View-based Human Face Detection. C.B.C.L., Paper No: 112, MIT (1994)Google Scholar
  8. 8.
    Moghaddam, B., Pentland, A.: Face Recognition using View-Based and Modular Eigenspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)CrossRefGoogle Scholar
  9. 9.
    Rowley, H., Baluja, S., Kanade, T.: Neural Network Based Face Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  10. 10.
    Adams, R., Bischof, L.: Seeded Region Growing. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)CrossRefMATHGoogle Scholar
  11. 11.
    Heisele, B., Ho, P., Wu, J., Poggio, T.: Face Recognition: Component-based versus Global Approaches. Computer Vision and Image Understanding 91, 6–21 (2003)CrossRefGoogle Scholar
  12. 12.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  13. 13.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)CrossRefGoogle Scholar
  14. 14.
    Terrillon, J.C., Shirazi, M.N., Akamatsu, S.: Comperative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 54–61 (2000)Google Scholar
  15. 15.
    Chiang, C.-C., Tai, W.-K., Yang, M.-T., Huang, Y.-T., Huang, C.-J.: A novel method for detecting lips, eyes and faces in real time. Real-Time Imaging 9 9, 277–287 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erhan AliRiza İnce
    • 1
  • Syed Amjad Ali
    • 1
  1. 1.Electrical and Electronic EngineeringEastern Mediterranean UniversityFamagustaNorth Cyprus

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