Face Recognition Issues in a Border Control Environment

  • Marijana Kosmerlj
  • Tom Fladsrud
  • Erik Hjelmås
  • Einar Snekkenes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Face recognition has greatly matured since the earliest forms, but still improvements must be made before it can be applied in high security or large scale applications. We conducted an experiment in order to estimate percentage of Norwegian people having one or more look-alikes in Norwegian population. The results indicate that the face recognition technology may not be adequate for identity verification in large scale applications. To survey the additional value of a human supervisor, we conducted an experiment where we investigated whether a human guard would detect false acceptances made by a computerized system, and the role of hair in human recognition of faces. The study showed that the human guard was able to detect almost 80% of the errors made by the computerized system. More over, the study showed that the ability of human guard to recognize a human face is a function of hair: false acceptance rate was significantly higher for the images where the hair was removed compared to where it was present.


Face Recognition Face Database Biometric System False Acceptance Rate Border Control 
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.
    ICAO: Biometrics Deployment of Machine Readable Travel Documents. ICAO TAG MRTD/NTWG. Technical Report, Version 1.9. Montreal (May 2003)Google Scholar
  2. 2.
    United States General Accounting Office: Technology Assessment: Using Biometrics for Border Security, November 14 (2002)Google Scholar
  3. 3.
    Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: FRVT 2002: Evaluation Report (March 2003)Google Scholar
  4. 4.
    Mansfield, A.J., Wayman, J.L.: Best Practices in Testing and Reporting Performance of Biometric Devices. Version 2.01 (August 2002)Google Scholar
  5. 5.
    Faculty of Computer and Information Science, University of Ljubljana, Slovenia (CVL FACE DATABASE)Google Scholar
  6. 6.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The Extended M2VTS Database. In: Second International Conference on Audio and Video-based Biometric Person Authentication (March 1999)Google Scholar
  7. 7.
    Martinez, A.M., Benavente, R.: The AR face database, CVC Tech. Report #24 (1998)Google Scholar
  8. 8.
    Kosmerlj, M.: Passport of the Future: Biometrics against Identity Theft? MSc thesis. Gjøvik University College, NISlab. Master’s thesis, June 30 (2004)Google Scholar
  9. 9.
    Beveridge, R., Bolme, D., Teixeira, M., Draper, B.:The CSU Face Identification Evaluation System User’s Guide: Version 5.0. Computer Science Department Colorado State University, May 1 (2003)Google Scholar
  10. 10.
    Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Transactions on circuits and systems for video technology 14 (January 2004)Google Scholar
  11. 11.
    Ross, A., Jain, A.: Information Fusion in Biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Ross, A.: Multibiometric Systems. Communications of the ACM 47 (January 2004)Google Scholar
  13. 13.
    Fladsrud, T.: Face Recognition Software in a border control environment: Non-zero-effort-attacks’ effect on False Acceptance Rate. MSc thesis. Gjøvik University College, NISlab. Master’s thesis, June 30 (2005)Google Scholar
  14. 14.
    Gjøvik University College,,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marijana Kosmerlj
    • 1
  • Tom Fladsrud
    • 1
  • Erik Hjelmås
    • 1
  • Einar Snekkenes
    • 1
  1. 1.NISlab, Department of Computer Science and Media TechnologyGjøvik University CollegeGjøvikNorway

Personalised recommendations