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Statistical performance evaluation of biometric authentication systems using random effects models.

Sinjini Mitra1, Marios Savvides, Anthony Brockwell

  • 1Information Sciences Institute, University of Southern California, Marina Del Rey, CA 90292, USA. mitra@isi.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 15, 2007
PubMed
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This study presents a new statistical method for evaluating biometric authentication systems. The approach uses a hierarchical random effects model to predict error rates and false alarm probabilities, improving performance assessment for diverse populations.

Area of Science:

  • Biometrics
  • Statistical Modeling
  • Machine Learning

Background:

  • Biometric authentication systems are increasingly common.
  • Accurate performance evaluation is crucial for these systems.
  • Existing methods may not generalize to new populations.

Purpose of the Study:

  • Introduce a novel statistical method for biometric system performance evaluation.
  • Predict error rates and false alarm probabilities.
  • Enable performance prediction for new or larger subject groups.

Main Methods:

  • Hierarchical random effects model.
  • Bayesian inference techniques.
  • Posterior predictive distributions for error rate prediction.

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Main Results:

  • The method predicts biometric system performance using explanatory variables.
  • It allows for error rate prediction across different subject groups.
  • The model predicts false alarm probability as a function of watch-list size.

Conclusions:

  • The proposed statistical method enhances the evaluation of biometric authentication systems.
  • It offers improved prediction capabilities for diverse populations and scenarios.
  • Applicable to various face recognition technologies, including filter-based, GMM-based, and facial asymmetry systems.