Multi-Modal Biometrics Fusion: Beyond Optimal Weighting

Kar Ann Toh; Wei Yun Yau

Multi-Modal Biometrics Fusion: Beyond Optimal Weighting

Kar Ann Toh, Wei Yun Yau

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002
Pages	788-792
Number of pages	5
Publication status	Published - 2002
Event	Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 - Singapore, Singapore Duration: 2002 Dec 2 → 2002 Dec 5

Publication series

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

Other

Other	Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002
Country	Singapore
City	Singapore
Period	02/12/2 → 02/12/5

All Science Journal Classification (ASJC) codes

Engineering(all)

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Cite this

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title = "Multi-Modal Biometrics Fusion: Beyond Optimal Weighting",

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.",

author = "Toh, {Kar Ann} and Yau, {Wei Yun}",

note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 ; Conference date: 02-12-2002 Through 05-12-2002",

year = "2002",

language = "English",

isbn = "9810474806",

series = "Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002",

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booktitle = "Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002",

}

Toh, KA & Yau, WY 2002, Multi-Modal Biometrics Fusion: Beyond Optimal Weighting. in Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002. Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002, pp. 788-792, Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002, Singapore, Singapore, 02/12/2.

Multi-Modal Biometrics Fusion : Beyond Optimal Weighting. / Toh, Kar Ann; Yau, Wei Yun.

Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002. 2002. p. 788-792 (Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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