Human Head Pose Estimation and Its Application in Unmanned Aerial Vehicle Control

  • Chun Fui Liew
  • Takehisa Yairi
Conference paper


This chapter proposes a fully automatic framework for real-time human head pose estimation from a monocular image based on geometric approach. Our method starts with Constraint Local Model-based (CLM-based) facial feature tracking. Combined with tracked facial feature locations and a statistical 3D human face model pre-captured from Kinect device, 3D translation and 3D orientation of head pose are estimated by Pose from Orthography and Scaling with Iterations (POSIT) algorithm. Since both CLM-based facial feature tracking and POSIT algorithm are fast, our method can achieve real-time head pose estimation with more than 15 frames/s (fps) in an Intel i5-3230 CPU 3.0 GHz processor without GPU acceleration. By using Kinect’s head pose estimation results as ground truth data, our estimation results show that head location and orientations can achieve tracking accuracy within 2 cm and 5° standard deviations, respectively.


Facial Feature Training Image Unmanned Aerial Vehicle Image Patch Face Model 
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  1. 1.
    Fu Y, Huang T (2007) hMouse: head tracking driven virtual computer mouse. In: IEEE workshop on applications of computer vision, Austin, TX, USA, 21–22 Feb 2007Google Scholar
  2. 2.
    Martins P, Batista J (2008) Single view head pose estimation. In: IEEE international conference on image processing, San Diego, CA, USA, 12–15 Oct 2008Google Scholar
  3. 3.
    Wu C, Aghajan H (2008) Head pose and trajectory recovery in uncalibrated camera networks –region of interest tracking in smart home applications. In: ACM/IEEE international conference on distributed smart cameras, Stanford University, CA, USA, 7–11 Sept 2008Google Scholar
  4. 4.
    Stiefelhagen R, Bernardin K, Ekenel H, Voit M (2008) Tracking identities and attention in smart environments—contributions and progress in the CHIL Project. In: IEEE international conference on automatic face and gesture recognition, Amsterdam, The Netherlands, 17–19 Sept 2008Google Scholar
  5. 5.
    Murphy-Chutorian E, Doshi A, Trivedi M (2007) Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation. In: IEEE intelligent transportation systems conference, Seattle, WA, USA, 30 Sept–3 Oct 2007Google Scholar
  6. 6.
    Ghaffari A, Rezvan M, Khodayari A, Vahidi-Shams A (2011) A robust head pose tracking and estimating approach for driver assistant system. In: IEEE international conference on vehicular electronics and safety, Beijing, China, 10–12 July 2011Google Scholar
  7. 7.
    Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. Trans Pattern Anal Mach Intell 31(4):607–626CrossRefGoogle Scholar
  8. 8.
    Saragih JM, Lucey S, Cohn JF (2011) Deformable model fitting by regularized landmark mean-shift. Int J Comput Vis 91(2):200–215CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Gross R, Matthews I, Cohn J, Kanade T, Baker S (2008) Multi-PIE. In: IEEE international conference on automatic face and gesture recognition, Amsterdam, The Netherlands, 17–19 Sept 2008Google Scholar
  10. 10.
    Cootes TF (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685CrossRefGoogle Scholar
  11. 11.
    Dementhon DF, Davis LS (1995) Model-based object pose in 25 lines of code. Int J Comput Vis 15(1–2):123–141CrossRefGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.University of TokyoTokyoJapan

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