An adaptive multimodal biometric management algorithm

Kalyan Veeramachaneni, Lisa Ann Osadciw, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


This paper presents an evolutionary approach to the sensor or 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, which is an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of adaptive, multimodal biometric management (AMBM) allows it to react in pseudoreal 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)
Pages (from-to)344-356
Number of pages13
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number3
StatePublished - Aug 2005


  • Multimodal biometrics
  • Multisensor fusion
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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