IJCB2014 tutorial - Biometric Menagerie banner

System Design and Performance Assessment: A Biometric Menagerie Perspective

IJCB2014 Tutorial
29 Sept 2014, 2pm-5pm
Norman Poh
Slide in pdf

Abstract

One of the major sources of variability in assessing the performance of a biometric system is the subject variability. Testing the same system on two disjoint populations of users almost always invariably yields two different results. This phenomenon was first described by Doddington et al when they evaluated speaker recognition systems in 1998. The users, or more precisely the statistical models associated with them, who are difficult to be recognised by a biometric system are given names such as goats and lambs; whereas users whose biometric traits are likely to be successful at impersonating others are called wolves. This phenomenon is dubbed “Doddington Zoo” and “Biometric Menagerie”. Subsequent studies then either aim at characterising the phenomenon, or at reducing the phenomenon that leads to better tailored decisions such as user-specific decision threshold and user-specific score calibration, and fusion strategies.

In this tutorial, we will describe biometric menagerie and explain how and why it has a direct impact on how the system performance is characterised; how confidence intervals can be estimated; and why performance prediction is difficult.

The significance of biometric menagerie is that it has impact on all biometric modalities. Furthermore, by reducing the phenomenon through user-specific strategies, a relative performance gain of about 30% has been observed for a unimodal biometric system; and up to 50% for a multimodal biometric system.

Video lecture

Talk outline

outline

1. The basics in biometrics performance reporting

  • False Acceptance Rate
  • False Rejection Rate
  • Equal Error Rate 

2. Characterising Biometric Menagerie

  • Doddington’s classification
  • Yager and Dustone’s classification
  • Some explanations and conjectures of the causes of Menagerie
  • Biometric Menagerie Index
  • User-ranking and its applications

3. Performance Intervals Estimation in the Presence of Biometric Menagerie

  • A two-step bootstrapping procedure that takes Biometric Menagerie into consideration
  • Worst-case scenario DET curves
  • Performance generalisation under different subject compositions and application settings

4. Reducing the Impact of Biometric Menagerie

  • User-specific decision threshold
  • User-specific score calibration
    • F-norm, Z-norm, T-norm
    • Group-based normalisation
    • Discriminative normalisation
  • User-specific fusion
    • Direct design of user-specific fusion
    • A two-step design of user-specific fusion
    • User-specific and modality-selective fusion via B-ratio

5. Open-ended questions involving Biometric Menagerie

  • Spoofing and Menagerie
  • Biometric Menagerie – an algorithmic, modality, or quality issue?
  • Estimating unimodal and multikodal biometric performance over time

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