Pattern Recognition and Image Analysis

, Volume 22, Issue 4, pp 541–545 | Cite as

Assessment of human head pose in human-computer interaction

  • S. I. Anishchenko
  • V. A. Osinov
  • D. G. Shaposhnikov
Applied Problems
  • 141 Downloads

Abstract

A system is described that estimates a user’s head position from a video image of it. The system includes three base algorithms: segmentation, detection of markers, and assessment of motion direction. The direction of head motion is determined by the dynamics of changing geometrical correlations between facial markers in the picture sequence. It is shown that the change in the angle formed by straight lines connecting the corners of the eyes and the tip of the nose have change dynamics similar to that of “yawing.” This system operates in real time (7 fps) and ensures high precision in assessing the direction of motions (p = 0.95).

Keywords

video image interface video snapshots 

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References

  1. 1.
    S. Anishenko, D. Shaposhnikov, R. Comley, and X. Gao, “Facial Image Segmentation Based on Mixed Colour Space,” in Proc. 15th Int. Conf. on Neurocybernetics (Rostov-on-Don, 2009), Vol. 2, pp. 231–234.Google Scholar
  2. 2.
    P. A. Bakaut, and G. S. Kolmogorov, “Image Segmentation: Domain Boundaries Detection,” Zarubezhn. Elektron. 10, 25–47 (1987).Google Scholar
  3. 3.
    X. W. Gao, S. Anishenko, D. Shaposhnikov, L. Podlachikova, S. Batty, and J. Clark, “High-Precision Detection of Facial Landmarks to Estimate Head Motions Based on Vision Models,” J. Comp. Sci. 3(7), 528–532 (2007).CrossRefGoogle Scholar
  4. 4.
    A. Jaimes and N. Sebe, “Multimodal Human-Computer Interaction: A Survey,” Comput. Vision Image Understand. 108(1–2), 116–134 (2007).CrossRefGoogle Scholar
  5. 5.
    P. Majaranta and K.-J. Räihä, “Text Entry by Gaze: Utilizing Eye-Tracking,” in Text Entry Systems: Mobility, Accessibility, Universality, Ed. by I. S. MacKenzie and K. Tanaka-Ishii (2007), pp. 175–187.Google Scholar
  6. 6.
    M. D. Fairchild, Color Appearance Models (Addison-Wesley, Reading, MA, 1998).Google Scholar
  7. 7.
  8. 8.
    E. L. Cascia, S. Sclaroff, and V. Athitsos, “Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3d Models,” Pattern Anal. Mach. Intelligence 22(4) (2000).Google Scholar
  9. 9.
    R. Cowie, E. Douglas-Cowie, K. Karpouzis, G. Caridakis, M. Wallace, and S. Kollias, “Recognition of Emotional States in Natural Human-Computer Interaction,” in Multimodal User Interfaces, Ed. by D. Tzovaras (Springer, 2008), pp. 119–153.Google Scholar
  10. 10.
    X. W. Gao, S. Anishenko, D. Shaposhnikov, L. Podlachikova, S. Batty, and J. Clark, “High-Precision Detection of Facial Landmarks to Estimate Head Motions Based on Vision Models,” J. Comput. Sci. 3(7), 528–532 (2007).CrossRefGoogle Scholar
  11. 11.
    Marr, D., Vision (W.H. Freeman, New York, 1982).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  • S. I. Anishchenko
    • 1
  • V. A. Osinov
    • 1
  • D. G. Shaposhnikov
    • 1
  1. 1.A. B. Kogan Research Institute of NeurocyberneticsSouthern Federal UniversityRostov-on-DonRussia

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