In this paper we present an application of agent technology to the problem of face recognition. With a new composite model consisting of multiple layers, the system can achieve high performance in terms of robustness and recognition in complex visual environmental conditions. The robustness of the complex face recognition system is enhanced due to integration with agent based paradigm, with more than 95% accuracy achieved under illumination, pose and expression variations of faces in images with multiple faces, and background objects. The results of preliminary findings are promising, suggesting further investigations in intelligent agent methodology for multimodal biometrics using fusion of face, gait, gesture, and voice biometric traits to person identity recognition problem in distributed scenarios such as video surveillance, health informatics, and crime investigation applications.


Face Recognition Face Image Face Detection Video Surveillance Biometric Trait 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deravi, F., Lockie, M.: Biometric Industry Report - Market and Technology Forecasts to 2003. Elsevier Advanced Technology (December 2000)Google Scholar
  2. 2.
    Sharma, D.: Proposal for a Multi-Agent Reasoning System Environment, School of ISE, University of CanberraGoogle Scholar
  3. 3.
    Jennings, N.R., Sycara, K., Wooldridge, M.: A Roadmap of Agent Research and Development. Autonomous Agents and Multi-Agent Systems, vol. 1, pp. 275–306. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  4. 4.
    Aslandogan, Y.A., Yu, C.T.: Diogenes: A Web Search Agent for Content Based Indexing of Personal Images. In: Proceedings of ACM SIGIR 2000, July 2000, Athens, Greece (2000)Google Scholar
  5. 5.
    Wisniewski, H.: Face Recognition and Intelligent Software Agents - an Integration System. Prepared statement for the U.S. Senate Committee on Commerce, Science and Transportation (May 12, 1999)Google Scholar
  6. 6.
    Chetty, G., Sharma, D.: Multmodal biometric fusion based on multi-agent architecture. In: 2007 WSEAS Int. Conf. On Computer Engineering And Applications (CEA 2007) (under review)Google Scholar
  7. 7.
    Aoki, Y., Hisatomi, K., Hashimoto, S.: Robust and Active Human Face Tracking Vision Using Multiple Information. In: Proceedings of SCI 1999 (World Multiconference on Systems, Cybermetics and Informatics), Orlando, August 1999, vol. 5, pp. 28–33 (1999)Google Scholar
  8. 8.
    Kiniry, J., Zimmerman, D.: Special Feature: A Hands-on Look at Java Mobile agentsGoogle Scholar
  9. 9.
    Lange, D.B., Oshima, M.: Programming and Deploying Java Mobile Agents with Aglets. Addison-Wesley, Reading (1998)Google Scholar
  10. 10.
    Cai, A.G., Yu, C.: Detecting Human Faces in Color Images. Image and Vision Computing 18(1), 63–75 (2000)MATHCrossRefGoogle Scholar
  11. 11.
    Kanade, T.: Picture Processing by Computer Complex and Recognition of Human Faces. Technical report, Kyoto University, Dept. of Information Science (1973)Google Scholar
  12. 12.
    Lades, M., Vorbruggen, J.C., Buhmann, J.: Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers 42(3) (March 1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Girija Chetty
    • 1
  • Dharmendra Sharma
    • 1
  1. 1.School of Information Sciences and EngineeringUniversity of CanberraAustralia

Personalised recommendations