Multi-Modal Biometrics Fusion: Beyond Optimal Weighting

Kar Ann Toh, Wei Yun Yau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The Multivariate polynomials model provides an effective way to describe complex nonlinear input-output relationships as 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 prohibitive number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomials model to circumvent the dimensionality problem with some compromise in the approximation capability. When applied to multi-modal biometrics fusion, this model is demonstrated to improve the combined classification performance in terms of classification accuracy.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002
Pages788-792
Number of pages5
Publication statusPublished - 2002
EventProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 - Singapore, Singapore
Duration: 2002 Dec 22002 Dec 5

Publication series

NameProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002

Other

OtherProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002
CountrySingapore
CitySingapore
Period02/12/202/12/5

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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