The Role of Statistical Models in Biometric Authentication

  • Sinjini Mitra
  • Marios Savvides
  • Anthony Brockwell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

The current paper demonstrates the role of statistical models in authentication tasks – both in system development and in performance evaluation. We first introduce a model-based face authentication system based on the Fourier domain phase using Gaussian Mixture Models (GMM) which yields verification error rates as low as 0.3% on a face database of 65 individuals with extreme illumination variations. We then present a statistical framework for predicting authentication error rates for future populations in a rigorous way. This is in contrast to most evaluation protocols used today that are based on observational studies and valid only for the databases at hand. Applications establish that our model-based approach has better predictive performance than an existing state-of-the-art authentication technique.

Keywords

Face Recognition False Alarm Rate Training Image Authentication Scheme Equal Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sinjini Mitra
    • 1
  • Marios Savvides
    • 2
  • Anthony Brockwell
    • 1
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburgh
  2. 2.Electrical and Computer Engineering DepartmentCarnegie Mellon UniversityPittsburgh

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