Over-complete feature generation and feature selection for biometry

  • Authors:
  • Alessandra Lumini;Loris Nanni

  • Affiliations:
  • DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

In this paper a novel method for obtaining an appropriate representation of patterns is presented. The information is extracted using an over-complete global feature combination, and then the most useful features are selected by sequential forward floating selection (SFFS). This new method has been tested in two problems: trained integration of iris and face biometrics; on-line signature verification system based on global information and a one-class classifier (Parzen Window Classifier). To the best of our knowledge, this is the first work that studies and proposes a set of ''artificial'' features for combining biometric matchers, created starting from the scores of the matchers. We show that a classifier trained on such set of features gains a noticeable performance improvement with respect to fixed fusion rules and other trained fusion methods. Moreover, we show that an on-line signature matcher based on the ''artificial'' features gains a noticeable performance improvement with respect to a matcher based on the ''original'' global features.