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

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 (a) rankings of individual fingers with respect to Minutiae and Eigenfinger features; (b) fusion of multiinstance intra-feature (Minutiae or Eigenfinger) matching scores; (c) cross-feature compared to intra-feature performance; (d) optimal weights for weighted versions of five score-level fusion methods Max, Median, Min, Product and Sum, and (e) 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.