The modern world has seen a rapid evolution of the technology of
biometric authentication, prompted by an increaing urgency to ensure a
system's security. The need for efficient authentication systems has
skyrocketed since 9/11, and the proposed inclusion of digitized photos
in passports shows the importance of biometrics in homeland security
today. Based on a person's essentially unique biological traits, these
methods are potentially more reliable than traditional identifiers like
PINs and ID cards. This paper focuses on demonstrating the use
of statistical models in devising efficient authentication systems
today that are capable of handling real-life applications. First, we
propose a novel Gaussian Mixture Model-based face authentication
approach in the frequency domain by exploiting the well-known
significance of phase in face identification and illustrate that our
method is superior to the non-model based state-of-the-art system
called the Minimum Average Correlation Energy (MACE) filter in terms
of performance on a database of 65 people under extreme illumination
conditions. We then introduce a general statistical framework for
assessing the predictive performance of a biometric system (including
watch-list detection) and show that our model-based system outperforms
the MACE system in this regard as well. Finally, we demonstrate how
this framework can be used to study the watch-list performance of a
biometric system.
Keywords: authentication, biometrics, error rate, false alarms,
frequency, Gaussian mixture model, phase, performance evaluation,
random effects model, watch-list