Head Pose Estimation from Passive Stereo Images

  • M. D. Breitenstein
  • J. Jensen
  • C. Høilund
  • T. B. Moeslund
  • L. Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30°, the system achieves an accuracy of 83.6%.


Range Image Facial Variation Stereo System Pitch Rotation Stereo Match 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.


  1. 1.
    Breitenstein, M.D., Kuettel, D., Weise, T., Van Gool, L., Pfister, H.: Real-time face pose estimation from single range images. In: CVPR (2008)Google Scholar
  2. 2.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: An evaluation of multimodal 2D+3D face biometrics. PAMI 27(4), 619–624 (2005)CrossRefGoogle Scholar
  3. 3.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3d face recognition under varying facial expression. PAMI 28(10), 1695–1700 (2006)CrossRefGoogle Scholar
  4. 4.
    Colbry, D., Stockman, G., Jain, A.: Detection of anchor points for 3d face verification. In: A3DISS, CVPR Workshop (2005)Google Scholar
  5. 5.
  6. 6.
    Jones, M., Viola, P.: Fast multi-view face detection. Technical Report TR2003-096, Mitsubishi Electric Research Laboratories (2003)Google Scholar
  7. 7.
    Lu, X., Jain, A.K.: Automatic feature extraction for multiview 3D face recognition. In: FG (2006)Google Scholar
  8. 8.
    Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: FG (2000)Google Scholar
  9. 9.
    Morency, L.-P., Sidner, C., Lee, C., Darrell, T.: Head gestures for perceptual interfaces: The role of context in improving recognition. Artificial Intelligence 171(8-9) (2007)Google Scholar
  10. 10.
    Morency, L.-P., Sundberg, P., Darrell, T.: Pose estimation using 3D view-based eigenspaces. In: FG (2003)Google Scholar
  11. 11.
    Murphy-Chutorian, E., Doshi, A., Trivedi, M.M.: Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation. In: Intelligent Transportation Systems Conference (2007)Google Scholar
  12. 12.
    Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. PAMI (2008) (to appear)Google Scholar
  13. 13.
    Nasrollahi, K., Moeslund, T.: Face quality assessment system in video sequences. In: Workshop on Biometrics and Identity Management (2008)Google Scholar
  14. 14.
    Osadchy, M., Miller, M.L., LeCun, Y.: Synergistic face detection and pose estimation with energy-based models. In: NIPS (2005)Google Scholar
  15. 15.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: CVPR (2005)Google Scholar
  16. 16.
  17. 17.
    Sankaran, P., Gundimada, S., Tompkins, R.C., Asari, V.K.: Pose angle determination by face, eyes and nose localization. In: FRGC, CVPR Workshop (2005)Google Scholar
  18. 18.
    Seemann, E., Nickel, K., Stiefelhagen, R.: Head pose estimation using stereo vision for human-robot interaction. In: FG (2004)Google Scholar
  19. 19.
    Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report, Carnegie Mellon University (April 1991)Google Scholar
  20. 20.
    Xu, C., Tan, T., Wang, Y., Quan, L.: Combining local features for robust nose location in 3D facial data. Pattern Recognition Letters 27(13), 1487–1494 (2006)CrossRefGoogle Scholar
  21. 21.
    Yao, J., Cham, W.K.: Efficient model-based linear head motion recovery from movies. In: CVPR (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. D. Breitenstein
    • 1
  • J. Jensen
    • 2
  • C. Høilund
    • 2
  • T. B. Moeslund
    • 2
  • L. Van Gool
    • 1
  1. 1.ETH ZurichSwitzerland
  2. 2.Aalborg UniversityDenmark

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