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Neural network optimization for combinations in identification systems

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper we investigate the construction of combination functions in identification systems. In contrast to verification systems, the optimal combination functions for identification systems are not known. In this paper we represent the combination function by means of a neural network and explore different methods of its training, so that the identification system performance is optimized. The modifications are based on the principle of utilizing best impostors from each training identification trial. The experiments are performed on score sets of biometric matchers and handwritten word recognizers. The proposed combination methods are able to outperform the likelihood ratio, which is optimal combination method for verification system, as well as, weighted sum combination method optimized for best performance in identification systems.

Original languageEnglish
Title of host publicationMultiple Classifier Systems - 8th International Workshop, MCS 2009, Proceedings
Pages418-427
Number of pages10
DOIs
StatePublished - 2009
Event8th International Workshop on Multiple Classifier Systems, MCS 2009 - Reykjavik, Iceland
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5519 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Multiple Classifier Systems, MCS 2009
Country/TerritoryIceland
CityReykjavik
Period06/10/0906/12/09

Keywords

  • Biometric matchers
  • Classifier combination
  • Identification system
  • Neural network

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