A new framework for adaptive multimodal biometrics management

Ajay Kumar Pathak, Vivek Kanhangad, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

77 Citations (Scopus)


This paper presents a new evolutionary approach for adaptive combination of multiple biometrics to ensure the optimal performance for the desired level of security. The adaptive combination of multiple biometrics is employed to determine the optimal fusion strategy and the corresponding fusion parameters. The score-level fusion rules are adapted to ensure the desired system performance using a hybrid particle swarm optimization model. The rigorous experimental results presented in this paper illustrate that the proposed score-level approach can achieve significantly better and stable performance over the decision-level approach. There has been very little effort in the literature to investigate the performance of an adaptive multimodal fusion algorithm on real biometric data. This paper also presents the performance of the proposed approach from the real biometric samples which further validate the contributions from this paper.
Original languageEnglish
Article number5412146
Pages (from-to)92-102
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Issue number1
Publication statusPublished - 1 Mar 2010


  • Adaptive biometrics security
  • Hybrid particle swarm optimization
  • Multimodal biometrics
  • Nonlinear fusion
  • Score-level dynamic fusion

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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