An evolutionary algorithm based approach for dynamic thresholding in multimodal biometrics

Kalyan Veeramachaneni, Lisa Ann Osadciw, Pramod Varshney

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper presents an evolutionary approach to the sensor management of a biometric security system that improves robustness. Multiple biometrics are fused at the decision level to support a system that can meet more challenging and varying accuracy requirements as well as address user needs such as ease of use and universality better than a single biometric system or static multimodal biometric system. The decision fusion rules are adapted to meet the varying system needs by particle swarm optimization, an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of AMBM allows it to react in pseudo-real-time to changing security needs as well as user needs. Error weights are modified to reflect the security and user needs of the system. The AMBM algorithm selects the fusion rule and sensor operating points to optimize system performance in terms of accuracy.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsDavid Zhang, Anil K. Jain
PublisherSpringer Verlag
Pages671-677
Number of pages7
ISBN (Print)3540221468, 9783540221463
DOIs
StatePublished - Jan 1 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Veeramachaneni, K., Osadciw, L. A., & Varshney, P. (2004). An evolutionary algorithm based approach for dynamic thresholding in multimodal biometrics. In D. Zhang, & A. K. Jain (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 671-677). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3072). Springer Verlag. https://doi.org/10.1007/978-3-540-25948-0_91