A principled approach to score level fusion in multimodal biometric systems

  • Authors:

  • Sarat C. Dass;Karthik Nandakumar;Anil K. Jain

  • Affiliations:

  • Department of Statistics and Probability, Michigan State University, MI;Department of Computer Science and Engineering, Michigan State University, MI;Department of Computer Science and Engineering, Michigan State University, MI

  • Venue:
  • AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
  • Year:
  • 2005

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Abstract

A multimodal biometric system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. We propose an optimal framework for combining the matching scores from multiple modalities using the likelihood ratio statistic computed using the generalized densities estimated from the genuine and impostor matching scores. The motivation for using generalized densities is that some parts of the score distributions can be discrete in nature; thus, estimating the distribution using continuous densities may be inappropriate. We present two approaches for combining evidence based on generalized densities: (i) the product rule, which assumes independence between the individual modalities, and (ii) copula models, which consider the dependence between the matching scores of multiple modalities. Experiments on the MSU and NIST multimodal databases show that both fusion rules achieve consistently high performance without adjusting for optimal weights for fusion and score normalization on a case-by-case basis.