Performance Prediction for Multimodal Biometrics

Authors:
Rong Wang;Bir Bhanu
Affiliations:
University of California, Riverside;University of California, Riverside
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 2

Multimodal biometrics: state of the art in fusion techniques

International Journal of Biometrics
An integrated authentication framework based on multi-tier biometrics

International Journal of Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sensor fusion is commonly used to improve the detection and recognition performance of a pattern recognition system. In this paper we propose a prediction model to predict the performance of a sensor fusion system. In particular, we answer two questions associated with the performance prediction in a sensor fusion system: (a) Given the characteristics of the individual sensors how can we predict the performance of the fusion system? (b) How good the prediction is? We provide the Cramer-Rao bounds for the prediction model. We carry out experiments on the publicly available database XM2VTS that has speech and face data.