Single-sensor multi-instance fingerprint and eigenfinger recognition using (weighted) score combination methods

  • Authors:

  • Andreas Uhl;Peter Wild

  • Affiliations:

  • Department of Computer Sciences, University of Salzburg, A-5020 Salzburg, Austria.;Department of Computer Sciences, University of Salzburg, A-5020 Salzburg, Austria

  • Venue:
  • International Journal of Biometrics
  • Year:
  • 2009

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Abstract

When multiple instances of single biometrics can be acquired from a single input simultaneously, a multiple-step acquisition at additional transaction time cost can be avoided. We present a rotation-invariant, peg-free multi-instance fingerprint- and eigenfinger-based biometric system extracting multiple features from a palmar scan of the hand. Our evaluation targets: (1) rankings of individual fingers with respect to minutiae and eigenfinger features; (2) fusion of multi-instance intra-feature (minutiae or eigenfinger) matching scores; (3) cross-feature compared to intra-feature performance; (4) optimal weights for weighted versions of five score-level fusion methods – max, median, min, product and sum and (5) aspects of computational demands for hand-based identification discussing the usage of serial classifier combinations instead of classically employed parallel ones. We examine results of an experimental approach to the problem of finding a suitable fusion method by investigating the effect of matcher-specific combination weights on recognition accuracy and compare cross-feature and intra-feature score combinations.