A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion

Kar Ann Toh, Wei Yun Yau, Xudong Jiang

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

The multivariate polynomial model provides an effective way to describe complex nonlinear input-output relationships since it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high-dimensional and high-order problems, multivariate polynomial regression becomes impractical due to its huge number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomial model to circumvent the dimensionality problem with some compromise in its approximation capability. In multimodal biometrics and many classifiers fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, this reduced multivariate polynomial model can be used to combine these classifiers in the next level of classification taking their outputs as the inputs to the reduced multivariate polynomial model. The model is first applied to a well-known pattern classification problem to illustrate its classification capability. The reduced multivariate polynomial model is then applied to combine two biometric verification systems with improved receiver operating characteristics performance as compared to an optimal weighing method and a few commonly used classifiers.

Original languageEnglish
Pages (from-to)224-233
Number of pages10
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume14
Issue number2
DOIs
Publication statusPublished - 2004 Feb

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion'. Together they form a unique fingerprint.

  • Cite this