A face and speech biometric verification system using a simple Bayesian structure

Andrew B.J. Teoh, S. A. Samad, A. Hussain

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1121-1137
Number of pages17
JournalJournal of Information Science and Engineering
Volume21
Issue number6
Publication statusPublished - 2005 Nov 1

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

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