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Proceedings Paper

Identity verification through the fusion of face and speaker data
Author(s): John G. Keller; Steven K. Rogers; Dennis W. Ruck; Mark E. Oxley
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Paper Abstract

Two verification systems, face and speaker, are fused to form a single identity verification system. The Karhunen-Loeve Transform (KLT) is used for dimensional reduction, and a back- propagation neural net is used for classification. Verification involved training a net for each individual in the database for two classes of outputs, `Joe' or `not Joe.' The base speaker identification system used Cepstral analysis for feature extraction and a distortion measure for classification. Verification in this case involved performing the KLT on the Cepstral coefficients and then classifying using a two-class neural net for each individual. KLT feature reduction is compared to alternative linear methods, and the KLT is found to provide superior performance. The fusion of the two base verification systems is shown to provide superior performance over either system alone.

Paper Details

Date Published: 2 March 1994
PDF: 9 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.170010
Show Author Affiliations
John G. Keller, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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