Tracking and Recognition of Multiple Faces at Distances

  • Rong Liu
  • Xiufeng Gao
  • Rufeng Chu
  • Xiangxin Zhu
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Many applications require tracking and recognition of multiple faces at distances, such as in video surveillance. Such a task, dealing with non- cooperative objects is more challenging than handling a single face and than tackling a cooperative user. The difficulties include mutual occlusions of multiple faces and arbitrary head poses. In this paper, we present a method for solving the problems and a real-time system implementation. An appearance model updating mechanism is developed via Gaussian Mixture Models to deal with tracking under head rotation and mutual occlusion. Face recognition based on video sequence is then performed to get the identity information. Through fusing the tracking and recognition information, the performance of them are both improved. A real-time system for multi-face tracking and recognition at distances is presented. The system can track multiple faces under head rotations, and deal with total occlusion effectively regardless of the motion trajectory. It is also able to recognize multi-persons simultaneously. Experimental results demonstrate promising performance of the system.


Face Recognition Gaussian Mixture Model Local Binary Pattern Head Rotation Total Occlusion 
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.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Prince, S., Elder, J., Hou, Y., Sizinstev, M., Olevskiy, E.: Towards face recognition at a distance. In: Proc. IET Conference on Security (2006)Google Scholar
  3. 3.
    Chen, M.L., Kee, S.: Head tracking with shape modeling and detection. Computer and Robot Vision, 483–488 (2005)Google Scholar
  4. 4.
    Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. Computer Vision and Pattern Recognition, 232–237 (1998)Google Scholar
  5. 5.
    Yang, T., Li, S.Z., Pan, Q., Li, J., Zhao, C.: Reliable and fast tracking of faces under varying pose. Automatic Face and Gesture Recognition, 421–426 (2006)Google Scholar
  6. 6.
    Niu, W., Jiao, L., Han, D., Wang, Y.F.: Real-time multiperson tracking in video surveillance. AInformation, Communications and Signal Processing, 1144–1148 (2003)Google Scholar
  7. 7.
    Jin, Y.G., Mokhtarian, F.: Towards robust head tracking by particles. Image Processing (2005)Google Scholar
  8. 8.
    Lerdsudwichai, C., Abdel-Mottaleb, M., Ansari, A.: Tracking multiple people with recovery from partial and total occlusion. Pattern Recognition 38, 1059–1070 (2005)CrossRefGoogle Scholar
  9. 9.
    McKenna, S.J., Gong, S., Raja, Y.: Face recognition in dynamic scenes. British Machine Vision (1997)Google Scholar
  10. 10.
    Krueger, V., Zhou, S.: Examplar-based face recogntion from video. Automatic Face and Gesture Recognition (2002)Google Scholar
  11. 11.
    Li, S.Z., Zhu, V., Zhang, L., Blake, Z.Q., Zhang, A., Shum, H.J.: Statistical learning of multi-view face detection. In: European Conference on Computer Vision, Copenhagen, Denmark (2002)Google Scholar
  12. 12.
    Li, S.Z., Chu, R.F., Liao, S.C., Zhang, L.: Illumination Invariant Face Recognition Using Near-infrared Images. IEEE transaction on Pattern Analysis and Machine Intelligence, April 2007 (to appear)Google Scholar
  13. 13.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE transaction on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Robust Real-time Object Detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    VioMoghaddam, B., Nastar, C., Pentland, A.: A Bayesian similarity measure for direct image matching. Media Lab Tech. Report No. 393, vol. 57, MIT (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rong Liu
    • 1
  • Xiufeng Gao
    • 1
  • Rufeng Chu
    • 1
  • Xiangxin Zhu
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu Beijing 100080China

Personalised recommendations