Adaptive management of multimodal biometrics fusion using ant colony optimization

Ajay Kumar Pathak, Amioy Kumar

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)


This paper presents a new approach for the adaptive management of multimodal biometrics to meet a wide range of application dependent adaptive security requirements. In this work, ant colony optimization (ACO) is employed for the selection of key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment of multimodal biometrics systems. Particle swarm optimization (PSO) has been widely utilized for the optimal selection of these parameters in the earlier attempts in the literature [Veeramachaneni et al., 2005] and [Kumar et al., 2010]. However, in PSO these parameters are computed in continuous domain while they are assumed to be better represented as discrete variables [Kumar et al., 2010]. This paper therefore proposes the use of ACO, in which discrete biometric verification parameters are computed to ensure the optimal performance from the multimodal biometrics system. The proposed ACO based framework is also extended to the pattern classification approach where fuzzy binary decision tree (FBDT) is utilized for two-class biometrics verification. The experimental results are presented on true multimodal systems from various publicly available databases; IITD databases of palmprint and iris, XM2VTS database of speech and faces, and the NIST BSSR1 databases of faces and fingerprint images. Our experimental results presented in this paper suggest that (i) ACO based approach is capable of operating on significantly small error rates in comparison to the widely employed PSO for automated selection of biometrics fusion rules/parameters, (ii) the score-level fusion yields better performance with lower error rate in comparison to the decision level fusion, and finally (iii) the FBDT based classification approach delivers considerably superior performance for the adaptive biometrics verification.
Original languageEnglish
Pages (from-to)49-63
Number of pages15
JournalInformation Fusion
Publication statusPublished - 1 Nov 2016


  • ACO
  • Adaptive biometric verification
  • FBDT
  • PSO
  • Score-level fusion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture


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