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3D Head Position Estimation Using a Single Omnidirectional Camera for Non-intrusive Iris Recognition

  • Kwanghyuk Bae
  • Kang Ryoung Park
  • Jaihie Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

This paper proposes a new method of estimating 3D head positions using a single omnidirectional camera for non-intrusive biometric systems; in this case, non-intrusive iris recognition. The proposed method has two important advantages over previous research. First, previous researchers used the harsh constraint that the ground plane must be orthogonal to the camera’s optical axis. However, the proposed method can detect 3D head positions even in non-orthogonal cases. Second, we propose a new method of detecting head positions in an omnidirectional camera image based on a circular constraint. Experimental results showed that the error between the ground-truth and the estimated 3D head positions was 14.73 cm with a radial operating range of 2-7.5 m.

Keywords

Ground Plane Head Position Distance Error Iris Recognition Head 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.
    Zhou, X., Collins, R., Kanade, T., Metes, P.: A Master-Slave System to Acquire Biometric Imagery of Humans at Distance. ACM Intern. Work. on Video Surveillance (2003)Google Scholar
  2. 2.
    Jankovic, N., Naish, M.: Developing a Modular Active Spherical Vision System. In: Proc. IEEE Intern. Conf. on Robotics and Automation, pp. 1246–1251 (2005)Google Scholar
  3. 3.
    Greiffenhagen, M., Comaniciu, D., Niemann, H., Ramesh, V.: Design, Analysis and Engineering of Video Monitoring Systems: An Approach and a Case Study. Proc. of IEEE on Third Generation Surveillance Systems 89(10), 1498–1517 (2001)Google Scholar
  4. 4.
    Chen, X., Yang, J., Waibel, A.: Calibration of a Hybrid Camera Network. In: Proc. of ICCV, pp. 150–155 (2003)Google Scholar
  5. 5.
    Cui, Y., Samarasckera, S., Huang, Q., Greiffenhagen, M.: Indoor monitoring via the collaboration between a peripheral sensor and a foveal sensor. IEEE Work on Surveillance, 2–9 (1998)Google Scholar
  6. 6.
    Fancourt, C., Bogoni, L., Hanna, K., Guo, Y., Wiles, R.: Iris Recognition at a Distance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1–13. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Guo, G., Jones, M., Beardsley, P.: A system for automatic iris capturing, Technical Report TR2005-044 Mitsubishi Electric Research Laboratories (2005)Google Scholar
  8. 8.
    Iris recognition on the move, Biometric Technology today (November/December 2005)Google Scholar
  9. 9.
    Bae, K., Lee, H., Noh, S., Park, K., Kim, J.: Non-intrusive Iris Recognition Using Omnidirectional Camera. In: ITC-CSCC 2004 (2004)Google Scholar
  10. 10.
    Benosman, R., Kang, S.: Panoramic Vision: Sensors, Theory and Applications. Springer, Heidelberg (2001)MATHGoogle Scholar
  11. 11.
    Geyer, C., Daniilidis, K.: Paracatadioptric camera calibration. IEEE Transactions on PAMI 24(5), 687–695 (2002)Google Scholar
  12. 12.
    Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.: PLnder: Real-time tracking of the human body. In: Proc. Automatic Face and Gesture Recognition, pp. 51–56 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwanghyuk Bae
    • 1
    • 3
  • Kang Ryoung Park
    • 2
    • 3
  • Jaihie Kim
    • 1
    • 3
  1. 1.Department of Electrical and Electronic EngineeringYonsei UniversitySeoulSouth Korea
  2. 2.Division of Media TechnologySangmyung UniversitySeoulSouth Korea
  3. 3.Biometrics Engineering Research Center (BERC) 

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