The role of statistical models in biometric authentication

Authors:
Sinjini Mitra;Marios Savvides;Anthony Brockwell
Affiliations:
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA;Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA;Department of Statistics, Carnegie Mellon University, Pittsburgh, PA
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

Citing 6
Cited 0

Discrete-time signal processing

Discrete-time signal processing
Minimax entropy principle and its application to texture modeling

Neural Computation
The CMU Pose, Illumination, and Expression (PIE) Database

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
"Eigenphases vs. Eigenfaces"

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Deformable templates for face recognition

Journal of Cognitive Neuroscience
"Corefaces"- robust shift invariant PCA based correlation filter for illumination tolerant face recognition

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

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.