The modern world has seen a rapid evolution of the technology of biometric authentication, prompted by an increasing 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.