Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences


Bioinformatics is the name given to statistical and computational approaches used to glean understanding from large data sets in molecular biology. Recent developments in genomic and molecular research technologies, combined with developments in information technologies have produced a tremendous amount of excitement in the research community. Major research efforts include genome-wide association studies, the study of copy number variation, gene finding, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. Statistics faculty interested in computational biology often work in collaboration with the Computational Biology Department at University of Pittsburgh Medical School.

Devising Face Authentication System and Performance Evaluation Based on Statistical Models

Sinjini Mitra, Anthony Brockwell, Marios Savvides, Stephen E. Fienberg
Publication Date:
September, 2005

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.

There are currently no lab groups for this area of research.