Optimal weighting of bimodal biometric information with specific application to audio-visual person identification
Optimal weighting of bimodal biometric information with specific application to audio-visual person identification
A new method is proposed to estimate the optimal weighting parameter for combining audio (speech) and visual (face) information in person identification, based on estimating probability density functions (pdfs) for classifier scores under Gaussian assumptions. Performance comparisons with real and simulated data indicate that this method has advantages in reducing bias and variance of the estimation relative to other methods tried, so achieving a robust estimator of the optimal weighting parameter. Another contribution is that we propose the bootstrap method to compare performances of different algorithms for estimating the optimal weighting parameter, so providing a strict criterion in comparing algorithms of this kind. Using simulated data, for which the pdf is controlled and known, we show that the advantages of the method hold up when the underlying Gaussian assumption is violated. The main drawback is that we have to choose an adjustable parameter, and it is not clear how this should best be done.
face recognition, speaker recognition, person identification, weighted sum rule, bootstrapping
172-182
Hu, R.
81186f54-7054-488e-a9ce-20cebb8e6dda
Damper, R.I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
April 2009
Hu, R.
81186f54-7054-488e-a9ce-20cebb8e6dda
Damper, R.I.
6e0e7fdc-57ec-44d4-bc0f-029d17ba441d
Hu, R. and Damper, R.I.
(2009)
Optimal weighting of bimodal biometric information with specific application to audio-visual person identification.
Information Fusion, 10 (2), .
(doi:10.1016/j.inffus.2008.08.003).
Abstract
A new method is proposed to estimate the optimal weighting parameter for combining audio (speech) and visual (face) information in person identification, based on estimating probability density functions (pdfs) for classifier scores under Gaussian assumptions. Performance comparisons with real and simulated data indicate that this method has advantages in reducing bias and variance of the estimation relative to other methods tried, so achieving a robust estimator of the optimal weighting parameter. Another contribution is that we propose the bootstrap method to compare performances of different algorithms for estimating the optimal weighting parameter, so providing a strict criterion in comparing algorithms of this kind. Using simulated data, for which the pdf is controlled and known, we show that the advantages of the method hold up when the underlying Gaussian assumption is violated. The main drawback is that we have to choose an adjustable parameter, and it is not clear how this should best be done.
More information
e-pub ahead of print date: 7 August 2008
Published date: April 2009
Keywords:
face recognition, speaker recognition, person identification, weighted sum rule, bootstrapping
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 264232
URI: http://eprints.soton.ac.uk/id/eprint/264232
PURE UUID: e3a4e45d-dfc3-41ce-a75b-6ea095279166
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Date deposited: 11 Jul 2007
Last modified: 14 Mar 2024 07:44
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Contributors
Author:
R. Hu
Author:
R.I. Damper
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