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Likelihood ratio-based biometric score fusion.

Karthik Nandakumar1, Yi Chen, Sarat C Dass

  • 1Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, East Lansing, MI 48824-1226, USA. nandakum@cse.msu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 18, 2007
PubMed
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This study introduces an optimal framework for combining scores from multiple biometric systems. The new method, using a likelihood ratio test, significantly improves accuracy over existing techniques.

Area of Science:

  • Biometrics
  • Pattern Recognition
  • Machine Learning

Background:

  • Individual biometric systems have limitations.
  • Multibiometric systems enhance performance by fusing data from multiple sources.
  • Existing score fusion techniques have limitations in handling diverse score distributions and correlations.

Purpose of the Study:

  • To propose a novel framework for optimal combination of match scores in multibiometric systems.
  • To address limitations of current fusion methods, including discrete score values, arbitrary distributions, score correlations, and sample quality.

Main Methods:

  • Developed a fusion framework based on the likelihood ratio test.
  • Modeled genuine and impostor match score distributions using finite Gaussian mixture models.

Related Experiment Videos

  • The framework is designed to handle discrete score values, arbitrary scales and distributions, score correlations, and varying sample quality.

    • Main Results:

    • The proposed fusion framework demonstrated consistently high performance across three multibiometric databases.
    • Achieved superior results compared to common score fusion techniques based on score transformation and classification.
    • The method effectively handles various challenges in multibiometric score fusion.

    Conclusions:

    • The likelihood ratio test-based fusion framework offers an optimal and generalizable approach for multibiometric systems.
    • This method provides a significant improvement in performance and robustness over existing score fusion techniques.
    • The framework's ability to handle diverse data characteristics makes it suitable for real-world applications.