Multimodal biometrics management using adaptive score-level combination

Ajay Kumar Pathak, Vivek Kanhangad, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

This paper presents a new evolutionary approach for adaptive combination of multiple biometrics to dynamically ensure the performance for the desired level of security. The adaptive combination of multiple biometrics is achieved at the matching score level. The score level fusion rules are adapted to ensure the required/desired system performance using particle swarm optimization. The experimental results presented in this paper illustrates two main advantages of the proposed score-level approach over the decision level approach; better performance and stable performance that require smaller number of iterations. There has not been any effort in the literature to investigate the performance of adaptive multimodal fusion algorithm on real biometric data. This paper also presents the performance of the proposed algorithm on real biometric data which further validates contributions from this paper.
Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
Publication statusPublished - 1 Dec 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: 8 Dec 200811 Dec 2008

Conference

Conference2008 19th International Conference on Pattern Recognition, ICPR 2008
Country/TerritoryUnited States
CityTampa, FL
Period8/12/0811/12/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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