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Biometric and Color Features Fusion for Face Detection and Tracking in Natural Video Sequences

  • Juan Zapata
  • Ramón Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

A system that performs the detection and tracking of a face in real-time in real video sequences is presented in this paper. The face is detected in a complex environment by a model of human colour skin. Very good results are obtained, since the colour segmentation removes almost all the complex background and it is realized to a very high-speed, making the system very robust. On the other hand, fast and stable real-time tracking is then achieved via biometric feature extraction of face using connected components labelling. Tracking does not require a precise initial fit of the model. Therefore, the system is initialised automatically using a very simple 2D face detector based on target ellipsoidal shape. Results are presented showing a significant improvement in detection rates when the whole sequence is used instead of a single image of the face. Experiments in tracking are reported.

Keywords

Video Sequence Face Detection Human Face Colour Segmentation Skin Detection 
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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References

  1. 1.
    Otsu, N.: A Threshold Selection Meted from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  2. 2.
    Hjelmas, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Yang, M.-H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  4. 4.
    Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 232–237 (1998)Google Scholar
  5. 5.
    La Cascia, M., Sclaro, S.: Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(4), 322–336 (2000)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: International Conference on Computer Vision, vol. 2, pp. 734–741 (2003)Google Scholar
  7. 7.
    Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of the International Conference on Computer Vision, pp. 555–562 (1998)Google Scholar
  8. 8.
    Jones, M.J., Rehg, J.M.: Statistical Color Models with Application to Skin Detection. Tech. Rep. Cambridge Research Laboratory (1998)Google Scholar
  9. 9.
    Haralick, R.M., Shapiro, L.: Computer and Robot Vision. 1, pp. 28–48. Addison Wesley, Reading (1992)Google Scholar
  10. 10.
    Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Machine Intell. 24(5), 696–706 (2002)CrossRefGoogle Scholar
  11. 11.
    Chai, D., Bouzerdoum, A.: A Bayesian approach to skin color classification in YCbCr color space. In: IEEE TENCON00, vol. 2, pp. 421–424 (2000)Google Scholar
  12. 12.
    Wong, K.W., Lam, K.M., Siu, W.C.: A robust scheme for live detection of human faces in color images. Signal Process. Image Commun. 18(2), 103–114 (2003)CrossRefGoogle Scholar
  13. 13.
    Pratt, W.K.: Digital Image Processing, 2nd edn. John Wiley and Sons, Chichester (1991)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Juan Zapata
    • 1
  • Ramón Ruiz
    • 1
  1. 1.Universidad Politécnica de Cartagena, Cartagena Murcia 30203Spain

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