Evaluation of Biometric Identification in Open Systems

  • Michael Gibbons
  • Sungsoo Yoon
  • Sung-Hyuk Cha
  • Charles Tappert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)


This paper concerns the generalizability of biometric identification results from small-sized closed systems to larger open systems. Many researchers have claimed high identification accuracies on closed system consisting of a few hundred or thousand members. Here, we consider what happens to these closed identification systems as they are opened to non-members. We claim that these systems do not generalize well as the non-member population increases. To support this claim, we present experimental results on writer and iris biometric databases using Support Vector Machine (SVM) and Nearest Neighbor (NN) classifiers. We find that system security (1-FAR) decreases rapidly for closed systems when they are tested in open-system mode as the number of non members tested increases. We also find that, although systems can be trained for greater closed-system security using SVM rather than NN classifiers, the NN classifiers are better for generalizing to open systems due to their superior capability of rejecting non-members.


Support Vector Machine Support Vector Machine Classifier Near Neighbor Radial Basis Function Kernel Equal Error Rate 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Michael Gibbons
    • 1
  • Sungsoo Yoon
    • 1
  • Sung-Hyuk Cha
    • 1
  • Charles Tappert
    • 1
  1. 1.Computer Science DepartmentPace UniversityPleasantvilleUSA

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