Keywords: bimodal biometrics, face module, speech module, simple bayesian structure, decision fusion
Received April 28, 2003; revised August 11, 2003; accepted July 8, 2004.
Communicated by Kuo-Chin Fan.
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Andrew B. J. Teoh, S. A. Samad* and A. Hussain*
Faculty of Information Science and Technology (FIST)
Multimedia University
75450, Melaka, Malaysia
E-mail: bjteoh@mmu.edu.my
*Electrical, Electronic and System Engineering Department
National University of Malaysia
43600, Bangi, Malaysia
E-mail: {salina; aini}@eng.ukm.my
Identity verification systems that use a mono modal biometric always have to contend
with sensor noise and limitations of the feature extractor and matcher, while combining
information from different biometrics modalities may well provide higher and
more consistent performance levels. However, an intelligent scheme is required to fuse
the decisions produced by the individual sensors. This paper presents a decision fusion
technique for a bimodal biometric verification system that makes use of facial and speech
biometrics. The decision fusion schemes considered have simple Bayesian structures
(SBS) that particularize the univariat Gaussian density function, Beta density function or
Parzen window density estimation. SBS has advantages in terms of computation speed,
storage space and its open framework. The performances of SBS is evaluated and compared
with that of other classical classification approaches, such as sum rule and Multilayer
Perceptron, on a bimodal database.
Received April 28, 2003; revised August 11, 2003; accepted July 8, 2004.
Communicated by Kuo-Chin Fan.