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Validating a biometric authentication system: sample size requirements.

Sarat C Dass1, Yongfang Zhu, Anil K Jain

  • 1Department of Statistics & Probability, Michigan State University, East Lansing, MI 48824, USA. sdass@msu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
PubMed
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This study introduces a new method for validating biometric system performance using multivariate copula models. It addresses the need for accurate confidence regions and sample size determination for ROC curves, accounting for correlated biometric data.

Area of Science:

  • Biometrics
  • Machine Learning
  • Statistical Modeling

Background:

  • Biometric authentication systems are widely used, but vendor performance claims often lack independent validation.
  • Existing methods for validating biometric performance and determining sample sizes are limited, particularly regarding the statistical independence assumption for multiple biometric acquisitions.

Purpose of the Study:

  • To develop a robust technique for validating claimed performance levels of biometric systems.
  • To establish methods for constructing confidence regions based on the Receiver Operating Characteristic (ROC) curve.
  • To determine the minimum number of biometric samples required for reliable performance validation.

Main Methods:

  • Developed a validation technique using multivariate copula models to handle correlated biometric acquisitions.

Related Experiment Videos

  • Applied the model to estimate confidence bands for ROC curves.
  • Determined the minimum sample size needed for desired confidence band width.

    • Main Results:

    • The proposed multivariate copula model effectively addresses the limitations of assuming statistical independence in biometric data.
    • The study provides a framework for estimating confidence bands around ROC curves for performance validation.
    • Methodology was illustrated using a fingerprint matching system, demonstrating practical application.

    Conclusions:

    • The developed technique offers a more accurate approach to validating biometric system performance by accounting for data correlation.
    • This method aids in establishing reliable confidence regions and determining appropriate sample sizes for ROC curve analysis.
    • The findings are crucial for independent verification of commercial biometric system claims.