Adaptive multimodal biometric fusion algorithm using particle swarm

Kalyan Veeramachaneni, Lisa Ann Osadciw, Pramod K. Varshney

Research output: Contribution to journalConference Articlepeer-review

16 Scopus citations


This paper introduces a new algorithm called "Adaptive Multimodal Biometric Fusion Algorithm"(AMBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system's accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to achieve the desired security level. The optimization function aims to minimize the cost in a Bayesian decision fusion. The particle swarm optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work. This algorithm is important to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired security level and switch between different rules and sensor operating points for varying needs.

Original languageEnglish (US)
Pages (from-to)211-221
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2003
EventMultisensor. Multisource Information Fusion: Architectures, Algorithms, and Applications 2003 - Orlando, FL, United States
Duration: Apr 23 2003Apr 25 2003


  • Bayesian decision fusion
  • Multimodal biometrics
  • Particle swarm optimization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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